Home

here - codinghands.co.uk

1. Unique Users 12 6 Change Scale Linear Logarithmic 9 Play Chart as Animation Select _J1day _ 25bahman 900daysotkisses _Jahoetshirtwoukisay _Jalgeria T Change X Axis Variable 8 Time Slider _ Jaristegui n 7 astonsbrthday v Trails Figure 5 25 Trends Component 98 2 Change Scale Linear Logarithmic Change the scale type for the Y axis Can be either linear or logarithmic 3 Chart Type Tabs Change the type of graph displayed The 3 main chart types are discussed in detail later in this section Scatter Bar or Line 4 Colour Variable Colouring can be used represent a third variable along with the X and Y axises Available options are Countries Unique Users Occurrences Same Colour all points are same colour or Unique Colour all points have different colours The gradient below indicates what each colour represents 5 Tag Select List Choose specify tags to show on each chart Click Deselect All to reset this list 6 Change Scale Linear Logarithmic Change the scale type for the X axis Can be either linear or logarithmic 7 Change X Axis Variable Allows the X axis variable to be changed Available options are Countries Occurrences and Unique Users Time and Order Alphabetical 8 Time Slider Scroll to watch graph points change over time Will only be visible if X axis is set to an
2. Stored in i l Ls lt day gt indices dailyindices ct J temporarily in Lo lt day gt indices lt indexType gt lt day gt indices dailyindices Figure 4 9 Pairwise Merge Tree Merging Implementation The hourly indices folder is read to check how many indices of the desired type are available for merging which are then stored as an array of directories Each index in this array of clean direc tories is then paired off with another into TweetMerge Worker threads TweetMerge Worker is a class implementing Runnable that performs each individual two index merger using the aforementioned Terrier method Once all indices inside the current clean directories array have been merged 5 new index direc tories will have been created where n is the number of original directories Each of these di rectories is named corresponding to the time range its index covers e g from lt unixTimestamp gt lt unixTimestamp gt These new directories are considered clean and added to the clean directory array whilst those just merged are considered dirty and ignored This process is repeated until the number of clean directories is 1 the final 24 hour index Once only 1 index directory remains all prior directories are deleted and the final index is renamed to its index type rather than time range Table 4 4 details the time taken to fully merge 24 hours of tweets the final index had 5 011 326 documents These benchmar
3. The difference in feedback from participants with very little or much experience was also interest ing The more experienced participants discussed the functionality of components in detail some even asking how certain components were developed They were also more focused on the scenar 10s set and provided constant commentary on their actions throughout the evaluation Novice users acted in a different way instead focusing on the potential possibilities that the system 137 D Twttr Novice No 3 60 2 40 5 Query responsiveness could be faster tools exp overwhelming at first captures detail comprehensively Twttr Novice No 3 50 3 50 Good at what it does very busy dis e pn covery important lots of options clut tered No user info simple to use trends component advanced interface affords clicking Powerful tool for business market re search well designed responsive at Twttr Active Low 6 85 1 15 I tools exp tractive No Twttr exp No 5 83 1 17 Appealing draws attention just have tools exp a go at it not like anything ever used needed user guide a 3 No Twttr exp No 6 85 1 15 7 Never seen anything like it lots of stuff tools exp you can do unique interesting visuali sations 4 Twttr Active High 4 66 2 34 tools exp E Twttr Inexp No 5 63 3 35 Distinguishable needs consistency tools exp maps very useful feels l
4. selecting date February 11 Once the results have appeared select the Compare tab in the Navigation Bar Select gaddafi from both drop down menus and click Compare 2 i E Since these results have been cached they should appear instantaneously Note the difference in number of occurences of the term gaddafi between these two dates Walkthrough 3 Hashtag Comparison Search by Tag 1 Search for valines selecting date February 14 2 Once the results have appeared search for up selecting date February 14 again 3 Once the results have appeared select the Compare tab in the navigation bar 4 Select valentines and up from the drop down lists and click Compare 5 Note the tags that both hashtags have in common from the Tagged With Component Walkthrough 4 Using Options 1 For large queries it is sometimes convenient to slightly reduce the comprehensiveness of results to obtain a large speed to display gain To see this search for fb selecting date February 1 2 You should notice it takes slightly longer than normal to return these results This is because of the processing time in certain components with particularly large hashtags 3 Once these results have loaded select Options from the navigation bar Reduce the Map Plotting to 20 Reduce the Sentiment Analysis to 20 6 Search for fb selecting date February 1 T
5. Ber Positive E Modecstaly Posteo Beth Positive Mig Negus E Noderstely Negative Bev negate Tagged With Duo Sentiment Analysis Geographical Duo Component Heatmap Duo In Context Duo Component Component Each component compares the results from both tags and offers a number of ways to see the differences See the Slashtag Query Components section for more detail Finally time queries will display the following Trends Component Place Device Component Unique Users an 3 of C ontcayecttnses Lamosa rtworidrey ti e Occurences Lint emanates 08 02 2011 gt a Daily Sentiment of All Tweets RT scooterbruan RT you love Gijustinbieber RT tawon02 MOM Al HS SS SAS http Co KADXZ Bolas AENA YRA EA BES toicigs MESE RT harukazechan USAF ADOLECE LERUENDA RENTO p CH WOLCUSADREOWMSUE w BALCM FTEDA B BENSLSEADACLELUMORBLALEUAS WER Links Component RT reainsane WTF Garota de 18 anos morre logo ap s dar o primeiro beijo hip migre mel RL Sentiment Volume Retweet Paths Component Trends Component e Trends Component follow trends pivot data zoom in on topics and points in time and more e Place Device Component see how people are tweeting and where from e Sentiment Volume Component find out the mood on Twitter e Retweet Paths Component follow retweets in any language as they propagate around the globe e Popular Links
6. Figure 4 7 Index Manager Thread Management 4 6 3 Data Cleansing Before indexing can begin each minute file must be cleansed to ensure the data it contains is valid In addition the 60 minute files are then checked for duplicates Any duplicate tweets across the files have the potential to cause indexing to fail by adding duplicate meta index keys for a given UID This risk is mitigated by adding a duplicate remover class to the indexing pipeline Run before indexing as a separate thread all tweets from each minute file are read from file and the UID of each tweet is then stored along with the file read from as a hashset Should a tweet UID already exist in the set then it is added to a duplicate list along with the file in which it appears Once this is complete the tweets are read again If the tweet UID and file name appear together in the duplicate list the tweet is discarded Otherwise the tweet object is written to a new file with an amended filename If no duplicate tweets were found in a given file then only the filename of the file is changed Using the filename tweet UID hashset means that there is no need to read in every tweet for an hour into memory Avoiding this pitfall was essential to ensure future scalability Holding even one hour s tweets in memory was unfeasible and during initial testing often resulted in the JVM running out of heap space Though more resources could have been provided this vertical approach to s
7. those which would profoundly improve the system but were not absolutely necessary became could haves and finally those which would be advantageous but not necessary became would like to haves The finalised MoSCoW categories are listed below e Must Have A robust data gathering application capable of managing the work involved in streaming millions of tweets into a easily retrievable storage format An application accessible in the browser that allows users to query the stored data in a usable engaging way A mechanism allowing users to search by topic or hashtag The results returned should give a comprehensive overview of that tag s usage from geographical temporal and magnitudinal perspectives A series of visualisations providing analysis of hashtag query results Envisioned ex amples included opinion analysis a map of tweets plotted by location and a cloud of co occurring hashtags 18 A significant amount of historical tweet data for long term temporal range queries e Should Have A mechanism allowing users to search across a temporal range The results returned should give an overview of Twitter activity across the temporal range A series of visualisations providing analysis of temporal range queries results Envi sioned examples include a trending topics graph the most popular links over a day and aggregate daily Twitter sentiment Support for queries to be compared
8. The User Data tab provides a scatter graph with details about those users who tweeted the query hashtag This can be used to establish whether a tag is being tweeted casually by many average users low follower following count or often by follower heavy users often news organisations celebrities businesses etc Basic Stats This section of the component provides basic information about the frequency of the query hashtag and the size of the collection on which the query was run This component is a work in progress in line with Pulse initial release status Slashtag Queries Slashtag queries in Pulse are used to compare two hashtags within specialised Duo versions of the hashtag query components These provide additional comparison functionality so that the results of two queries can be compared To make a slashtag query 1 Type a hashtag within the Query Box of the Query Bar followed by a slash followed by another hashtag Fwikileaks Fassange 2 Select a date to query using the Date Picker Note you cannot type directly into the Date Picker Use the calendar that appears on clicking the Date Picker to select a date Tue Feb 15 2011 18 3 Optional Set options for your Query within the Options Panel of the Navigation Bar see below 4 Click the Search Button in the Query Bar Slashtag Query Options Various options can be set for slashtag queries within the Options Panel under Hashtag Queries These
9. 1 rebecca black ii uknowuugly Mar 10 4am 10 4am Right now friday Figure 2 4 Trendistic The functionality provided by Trendistic is certainly useful but also fairly limited The data pre sented cannot be interacted with or pivoted in any way only the graph of usage over time is provided Cursory testing with the Trendistic site also highlighted some functional issues Often tweets failed to load and the interface does not provide any assistance or guidelines to users Tag usage over time is certainly an important measure of topic popularity but without supplemental information to place this quantitative data into context or even a way to compare multiple against each other there are few practical uses for the site beyond general interest 2 2 4 Twitter Sentiment Twitter Sentiment 7 is a sentiment analysis tool developed at Stanford University shown in Fig ure 2 5 It uses machine learning techniques and a maximum entropy classifier trained with a corpus of tweets featuring certain emoticons 49 to classify tweet text as either negative or positive Twitter Sentiment is perhaps the most interesting tool available for analysing Twitter output Un derstanding tweet sentiment has many practical uses such as brand management polling and as sisting user decision making Developing an effective sentiment analysis process would give Pulse a method of ranking topics or brands people places by establishing sen
10. Hashtag Queries Hashtag queries are the simplest type of queries within Pulse To make a hashtag query 1 Type a hashtag within the Query Box of the Query Bar Make sure to prefix the query with winning 2 Select a date to query using the Date Picker Note you cannot type directly into the Date Picker Use the calendar that appears on clicking the Date Picker to select a date Tue Feb 15 2011 3 Optional set options for your Query within the Options Panel of the Navigation Bar see below 4 Click the Search Button in the Query Bar Hashtag Query Options Various options can be set for hashtag queries within the Options Panel under Hashtag Queries Options of Results to Retrieve of Results to Analyse for Hashtag Queries Sentiment Retrieved Results 100 Whether or not to use the Senti Analy 100 POS tagger Use POS Parser of Results to Plot on Map Plotting 100 1 of Results to Retrieve Set the percentage of all results you want to be returned This can be useful for very popular tags where you don t require all of the results but rather a subset It also results in faster processing and querying 2 of Results to Analyse for Sentiment Set the percentage of all results to be processed by the sentiment analyser Again this is useful for tags with a large 12 number of results where only a subset is required It results in faster processing but obviously
11. This is made available as the update signal propagates to each component Once all components have been populated the system 1s ready for the next query 71 Temporal Range Queries Figure 5 16 illustrates the update mechanism used for temporal range queries send update i ey j lt results gt YeS signal to Response OF j components le Display error Figure 5 16 Time Query Update Mechanism Temporal range queries are not cached client side which makes managing them considerably easier than tag based queries Furthermore the response returned from the server is constructed specifi cally for use by temporal range query components with one hashmap of data for each Though more structured than the respond from a tag based query the data provided is still general enough to be re usable in custom components Update Challenges Implementing the slashtag query functionality meant a major rewrite of the way results were stored and components updated This is reflected in the somewhat disjointed way that requests and re sponses are converted from single to duo and back again across the course of the update workflow Similarly the addition of query caching later in development resulted in further modification of the main update process This eventually lead to the update mechanisms described above but each is only really suitable for its individual intended purpose The way this currently operates provides adequa
12. an exponentially increasing sleep time capped at 360 seconds e The connection is closed server side in this case there is likely an error occurring within the application such as duplicate connections laggy connection or invalid login details These events almost never occur Quality of Service Twitter makes the following three statements 38 regarding the quality of service to be expected of the Streaming API e Best Effort On occasion statuses may be missing from the stream e Unordered Statuses are streamed in no particular order but will tend to be k sorted where k is the number of tweets received in about 3 seconds 38 e Generally at least once Duplicates that do occur are likely due to operational events caused by Twitter Management of each of these constraints is discussed in Section 4 8 33 Sample Specification As previously mentioned Pulse downloads around 5 of all public tweets at Gardenhose access level However before arriving at the application this stream is pre filtered by Twitter Firstly all non public statuses are excluded such as statuses from protected accounts Secondly an unspecified quality filter is applied to certain accounts whose tweets are removed from the public stream Twitter does not specify what quality filters are aside from that they catch accounts posting repetitious or spam tweets stating Our search results will not include suspended accounts o
13. essential for a fully fledged analysis application Those participants with no prior experience using Twitter made some useful comments regarding ease of use and UI consistency In particular when typing in the query box a slash is used to differentiate between two tags However the options tag whitelist uses a comma for this same pur pose Syntax for similar tasks should be consistent across the whole application and only through evaluation can such errors be recognised and rectified As with the general word list useful was used twice by participants in their filtered word lists to describe Pulse One spoke about the many possible uses available and that it allows for deep investigation of periods of time Another user said that discovery was important to them analogising it to Wikipedia sometimes they are looking for a particular piece of information other occasions it s a tool to randomly discover new things The term cluttered appeared again in 2 participants filtered word lists One participant described it as having lots of stuff everywhere information overload Another remarked that it s hard 136 Organised Confusing er Powerful High Quality Dated Cluttered Simpie to UseBusy otish m p ress V Usable overwheminEngaging Usefy Unretned Time consuming y anced Unconventional intimidating Complex Effective Creative Figure 7 11 Filtered Adjective Cloud to
14. file in use error ured on daily index direc dices e Mean Time Between Failures MTBF how long does the application run between each crash restart cycle Two major versions of the application were developed one storing data in HBase the other using Terrier indices The HBase application ran for periods of 2 4 days before failure This often occurred due to an unexpected error within the HBase cluster Unfortunately early versions of the application had no logging capability and so such failures went unmissed for hours or in some cases days For this and the slow scan speed whilst searching the database HBase was discarded as a data store option though tweets streamed during this time have been backed up for future use The Terrier centric application was significantly more robust After component testing the applica tion was run from January 22nd to March 4th during which logs were created detailing the success or failure of each function in the system on an hourly basis Table 4 14 lists events that occured during this time The potential uptime for the system between 22 1 11 and 4 3 11 was 1008 hours The system was inactive or offline for a total of 81 hours giving a total uptime figure of 91 9 with a MTBF of 359 hours 2 weeks Part of the reason each downtime period was so lengthy was that no one was informed when a failure occurred The application requires a notification system to alert a human operator each time t
15. it does have its limitations jQuery is effectively a library of helper methods which augment and support other frameworks rather than providing an all encompassing structure in which applications can be built Query would have to be integrated into a larger development model such as one using PHP on a server to actually create a fully fledged application Development in raw JavaScript is also troublesome regardless of libraries used The only tools avail able for JavaScript development are browser based debuggers such as Firebug 16 which inspect 20 code at run time Unfortunately there are very few satisfactory development aides when writing JavaScript that provide anything like the support which IDEs such as Eclipse or Visual Studio pro vide Given the complexity of the proposed application it would be preferable to write in a language with more advanced development tools and utilities Other JavaScript libraries evaluated which were very similar to jQuery included YUI Yahoo User Interface library 42 and MooTools 29 Each provides JavaScript abstractions and interface widgets but neither has a overarching structure in which complex applications can be built Google Web Toolkit Google Web Toolkit GWT is a web development toolkit which lets developers quickly build com plex web applications The most interesting feature of GWT is the manner in which applications are written GWT features a Java to JavaScript compiler that all
16. provided that you feel describe the Pulse After this we d like to discuss why you selected these words e Questionnaire A final questionnaire to further establish your thoughts on Pulse Word Selection Please check the words that you feel best describe Pulse as many or as few as you like Once you ve done this please circle 5 that you feel are the most relevant OOoOddHAAODKAOAODOAODOADAADAAADAADAADAAAAAADAAAAAUAU A O Simple Intuitive Clear Bright Effective Usable Entertaining Unpredictable Old Patronising Familiar Secure Too technical Responsive Convenient Credible Fun Professional Empowering Busy Complex Energetic Meaningful Time saving Stimulating Dull Friendly Approachable Advanced Business like Ambiguous Illogical Unattractive Innovative Difficult BOOOODODOODODO D DO DO DODODDODODOOODODOO OOODODOODOODOCOoOOoOOoooooso Confusing Irrelevant Controllable Frustrating Time consuming Poor quality Stressful Trustworthy Reliable Effortless Relevant Fresh Ineffective Cutting edge System oriented Simplistic Straightforward Organised Ordinary Intimidating Boring Counter intuitive Powerful Fast Contradictory Vague Faulty Overwhelming Distracting Clean Annoying Rigid Efficient Misleading Attractive E OoOoOddODOAOAODAODAODAADAADAAAADAADAADAAAOAADAAOAAAA A O Creative Unconventional Consistent Predictable Accessible Dated Exciting Useful Incomprehensible
17. where we ask you to rate some element on a scale of 1 to 7 Usually the most positive response corresponds to 1 and the least positive to 7 Open opinion questions where we ask for your comments and opinions Please answer these as comprehensively as possible Whilst we would appreciate you completing the entirety of the form you may omit any question which you do not wish to answer Should you have any questions please speak to the demonstrator Questionnaire Social Network Experience 1 Which social networks sites or applications do you use if any Facebook C Bebo C Twitter C Last fm C Forums LinkedIn C Usergroup Other please MySpace C P 2 What are the main reasons you use social networking sites Keeping in touch with a Follow figures of a friends family interest ell aa bl C Discussion debate C life work home Sharing interests or l Stay informed about news E 3 How much of all your time spent online do you spend using social networking sites 1 All time gt 3 4 5 6 7 No time online online a a L L L L E Trend Analysis Experience 1 Have you ever used any trend analysis sites or tools please specify 2 For what reason do you use these tools Academic Academic Market Research Dune Purposes C Purposes C Computing Other Own Interest C Other 3 Do the tools have any particular features you heavily utilise 4 Which if any Twitter ana
18. 1000 sam ple tweets showed that the JSON entities were always under 5000 characters in length Terrier must be informed of the length of each meta index value through its indexer meta forward keylens property which in the case of JSON entities was set to 5000 However later request handling occasionally failed to parse certain tweet entities retrieved Investigation revealed that JSON en tities larger than 5000 characters were simply truncated when stored in the meta index This has since been fixed though older indices created before the bug was identified still contain malformed tweets A workaround for these indices is implemented by the core retrieval service described in Section 6 4 3 The TweetCollectionFromGZIP class ensures the correct documents are created for each indexer The class first checks the indexer mode to create the required document from the current tweet object This is then returned to Terrier for indexing Should a file have no further tweets the TweetCollectionFromGZIP class attempts to load the next tweet file if it exists If it does the next file of tweets is processed and indexed If not then all tweets have been indexed and the indexer joins the indexing manager This process is shown in Figure 4 8 4 6 5 Daily Indexing At the end of each day all of the hourly indices created need to be merged into a single daily index so as to hold an entire day s tweets It 1s these daily indices that are then later que
19. 57 58 59 60 I Ounis G Amati V Plachouras B He C Macdonald and C Lioma Terrier A High Performance and Scalable Information Retrieval Platform In Proceedings of ACM SIGIR 06 Workshop on Open Source Information Retrieval OSIR 2006 2006 D Robinson and K Coar The common gateway interface CGI version 1 1 RFC 3875 Internet Engineering Task Force October 2004 K Toutanova D Klein C D Manning and Y Singer Feature rich part of speech tagging with a cyclic dependency network In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology Volume l pages 173 180 Association for Computational Linguistics 2003 T Wilson P Hoffmann S Somasundaran J Kessler J Wiebe Y Choi C Cardie E Riloff and S Patwardhan OpinionFinder A system for subjectivity analysis In Proceedings of HLT EMNLP on Interactive Demonstrations pages 34 35 Association for Computational Linguistics 2005 Masahide Yuasa Keiichi Saito and Naoki Mukawa Emoticons convey emotions without cog nition of faces an fmri study In CHI 06 extended abstracts on Human factors in computing systems CHI EA 06 pages 1565 1570 New York NY USA 2006 ACM 149 Appendix A User Guide 150 User Guide sesos 06685 ee 00 66 ee 0606 66 40 66066 ee 0000 Initial draft Fixed minor errors added In Context component d
20. Duo Component The Tagged With Duo Component is based on the Tagged With Component with two notable differences the split tag cloud and the tag comparison details Split Tag Cloud Options List Split Tag Cloud Tag Comparison Details Cloud Divider The default view of this component shows each tag cloud with a divider in between This divider can be moved to focus on one or the other set of tags Functionality is as the standard Tagged With Component with the addition of the Tag Comparison option To view this select Tag Tag Comparison Details 7 Comparison from the Volume applet Volume qoogle apple over google 4 2 2 Options List The Tag Comparison view above shows all the tags that each query has in common and the difference in volume between each query 20 Location Duo Component Based on the Location Component this Duo version displays different coloured markers for each query Map Marker Selection Icons You can also choose to switch off markers for one or both locations by clicking the marker icon corresponding to the query hashtag in the component title bar above right Note for queries with a large number of results it is recommended that the Plotting option is reduced Tag Volume Duo Component The Duo version of this component shows the volume of each query every hour side by side as bars in a bar chart in the same style as the
21. Future Work The current implementation of this component is fairly basic in only providing an ordered list of links It would be interesting to understand what motivates users to tweet links and how the content of each link correlates with trending topics Links posted on Twitter could be used as a set of seeds for a web crawler providing a data source separate but related to Twitter for topic comparison A link index of pages crawled and standard tweet indices could be queried simultaneously with tools provided to analyse whether any parallels can be drawn between content on Twitter and content on the wider web 5 8 Conclusion The Pulse client application was built to provide a powerful flexible and usable interface through which users could easily perform queries in a number of ways It provides both topic and temporal range analysis of Twitter data through several components each tailored to provide a particular view of results The application can also be extended to incorporate new functionality and is highly configurable through options available to the user Data is also cached by the client for quick retrieval of past queries The next chapter discusses how data is managed and retrieved from the Pulse data store 109 Chapter 6 Pulse User Application Server Side Division of work between client and server in two tier systems such as Pulse allows for logical separation of functionality Traditionally with web applications
22. JavaScript features are seen For example the true speed of JSON parsing is not revealed until the application is deployed and operating solely from the browser 4 4 2 Tweet Structure An open connection to the Twitter Streaming API supplies a constant stream of real time JSON tweets which are parsed and processed by the application Each contains a complete record of 31 information about a given tweet the tweet itself location information user information and other relevant meta data A typical tweet entity is shown in Figure 4 5 place mll cooerdinates mull retweeted fal e in reply to status id mull id str 28039652140 truncated false in reply to status id str null source web favorited false in reply to user id str mull created at Thu Oct 21 16 02 46 0000 2010 contributors mull in reply to screen _name mull user f profile background image url http s twi profile link color 006454 description Gnip makes it really easy for y screen _name gnip id atr 16958B8753 profile text _color 333333 profile image _url http 1 1 a3 twimg com pr id 16958875 verified false pnotifications mmil ute of set 235200 he geo noll retweet_count mull id 28039652140 in reply to user id mull text what we ve been up to at Bonip deliver Figure 4 5
23. Responsiveness many web browsers such as Mozilla Firefox 30 feature an exe cution time limit for JavaScript code which when reached results in an error message being displayed to the user If this happens users may refresh the page ending any outstanding re quests or simply navigate away both severe anti goals of the system Using services avoids this allowing the UI and other work to perform asynchronously The client application requires the use of services at two points during each query to retrieve the initial result set from the Pulse data store and during the component update cycle Most compo nents manage processing on the client however the Sentiment Analysis component does require a supporting service to use an external Java library 6 4 Tweet Fetch Service The Tweet Fetch service provides the client application with the initial results needed to generate components for each query 6 4 1 Requirements The client application must have moderated access to the Pulse data store so that it can generate components in response to user queries However the structures used by the data store require Java libraries to open special manipulation to use and resources to store in memory These constraints mean that direct client interaction with the data store 1s near impossible The Tweet Fetch service should mask this complexity providing structured responses in reply to client requests The Tweet Fetch service must also have a
24. Runnable Data Cleanser Indexers Runnable Runnable To Client Application Figure 4 3 Data processing application architecture conducted by another application entirely though it relies on information placed in the datastore throughout the streaming process Throughout the rest of this chapter the individual architectures of each component are described in greater detail 4 2 2 Storage Having deciding on using Terrier as the primary retrieval mechanism a folder structure was required in which to organise Pulse data files indices logs and the tweets themselves This data is stored in a hierarchical folder structure shown in Figure 4 4 At the top level is the Pulse data directory This is the central data directory which is used by the user application for retrieving and displaying query results and analytics Within this directory are the daily data directories a day being from 00 00 00 23 59 59 containing tweets and indices for a given day These day directories are named using the epoch timestamp at midnight on the day they represent e g data from February 20th 2011 is in folder 1298164800000 Aside from the core tweet and index directories each daily folder also contains a text file of hyper links extracted from tweets on the day a log file detailing any errors in processing data for that day and a file of daily aggregated statistics see Section 4 9 4 3 Data Source Pulse utilises the Twitter Streaming
25. Sophisticated Hard to Use Motivating Insecure Flexible Easy to use Engaging Unrefined Impressive Desirable Obscure Expected Cluttered Inconsistent Appealing Comprehensive New Compelling High quality Stable Awkward Inadequate Non standard Understandable Satisfying Slow Usability 1 Ta Statement Strongly 2 3 4 5 6 Strongly Agree Disagree Overall I am satisfied with how easy it il was to use Pulse r It could effectively complete tasks set using Pulse a I could complete tasks set quickly using Pulse 4 I felt comfortable using Pulse 5 It was easy to learn how to use Pulse I believe I became productive quickly using Pulse The information such as help and other 7 documentation provided with Pulse was clear It was easy to find the information 1 needed 6 The information provided by Pulse was easy to understand A The information was effective in helping me complete the set tasks a The organisation of information within Pulse is clear 12 The user interface of Pulse is pleasant 13 liked using the interface of Pulse Oo Uo 0O 0 oO 0 0O O Oo oOo 0 0 0 OO O 0O U0 Oo DQO O l O O O Oo oOo oO l O oO O O O Oo oOo OO OO O OO O O U A Oo oOo ODO OO O O O O Oo Uo 0 OOO O U0 0O 14 Overall I was satisfied with Pulse Further Development 1 Compared to other trend analysis social media analysis too
26. Tag Query History or by selecting two tags for comparison in the Compare Panel These both store the cache ID associated with the query tag s for simple retrieval later The component manager fetches the cache corresponding to the ID provided and checks to see 1f the query had any results If not an error is displayed informing the user If so all components in turn are updated from cache using their update FromCache methods Cached Comparison Queries Figure 5 15 illustrates the update mechanism used when two tags from the cache are selected for comparison Compare cached o n Sel global request Construct queries request sent caches from 3 and response to DuoResponse by Navigation Bar cache cached request from 2 Compare Panel manager i response SingleResponses Send update signal using cache _ Components Updated Display message treat as single cached hashtag query Figure 5 15 Cached Comparison Update Mechanism Comparison queries effectively morph two hashtag queries into a single slashtag query for display within slashtag components Like slashtag searches checks must be done to see 1f both one or none of the queries had results As with failed slashtag queries should only one query tag have results an error message 1s displayed and the update mechanism operates in cached hashtag mode However 1f both query tags have results then a DuoResponse 1s created from the individual Sin gleResponses
27. The Place Device Component details how popular tweeting is in various countries using particular devices or software Switch to Places Change Day Displayed 400 000 f KM UberTwitter a A E Twitter for iPhone 200 000 Ml Twitter for BlackBerry W TweetDeck H Mobile Web 200 000 Hi Echofon Wi twitterfeed a _ I Keitai Web 100 000 Se Wi Twitter for Android E PL 3 SF SAwipple 0 AN l 12 02 2011 5 02 2011 09 02 2011 10 02 2011 View Devices 12 02 2011 13 02 2011 14 02 2011 15 02 2011 US Tweets 1341466 y R We gt Sy Sy A AS Sy PS gt PP e K xv gt we O Device Usage Chart Daily Geoheatmap 29 1210212011 5 eA The Place Device Component has two main views a line chart showing the number of tweets posted using a particular device or software and a geographical heatmap pain arar showing which countries were most active during a selected day To switch between views click the button in the top right corner above Device View The Device View tracks device or software used to post to Twitter across multiple days The days shown consist of the query day plus the number of additional days either side configured in the Time Query Options Should exclude web be selected in the options then web as a tweet source will not be shown Place View The Place View shows a geographical heatmap of tweet activity on a
28. TrendsMap http trendsmap com December 2010 6 TweetVolume http www tweetvolume com November 2010 7 Twitter Sentiment http twittersentiment appspot com December 2010 8 Twitter com ChloeS Status http twitter com ChloeS status 12172098017 April 2010 9 Twitter com XboxSupport Stream http twitter com xboxsupport March 2010 10 Alexa Twitter Site Info http www alexa com siteinfo twitter com March 2011 11 Apache Commons Collection Library http commons apache org collections March 2011 12 Apache HBase Home http hbase apache org March 2011 13 Apache HTTP Client http hc apache org httpcomponents client ga March 2011 14 Apache Tomcat http tomcat apache org March 2011 15 Deploying on a servlet container using RPC http code google com webtoolkit doc latest DevGuideDeploying html DevGuideDeployingServletContainerUsingRPC March 2011 16 Firebug Web Development Evolved http getfirebug com March 2011 17 GeoChirp http www geochirp com March 2011 146 18 19 20 21 22 23 24 Google Code Visualizations API MotionChart http code google com apis visualization documentation gallery motionchart html March 2011 Google Language API http code google com p gwt google apis wiki LanguageGettingStarted March 2011 Google Web Toolkit http code google com webtool1kit March 2011 GWT
29. Unix timestamp string for each day key dayString tagHashmap get key 100 dayDetails dayString split array length 5 each entry for each tag within dayLimit a Single var Lt Tag 110 Ww Lst and not Iin DList and within occurence Limit dataTable addRow for each variable in dayDetails dataTable setValue current row dayDetail value Once constructed the data table is passed to the Visualisations API which generates the visuali sation This visualisation is one of few implemented in Flash so a Flash browser plugin must be installed on the client side for this component to display correctly For easier viewing the component can also be enlarged by clicking the Enlarge button in the component title bar Options Multiple options can be set which customise the data displayed by the Trends Component These include e Daily Top Tag Limit the number of top hashtags per day to show in the component e Tag Minimum Maximum Occurences upper and lower boundaries for the number of oc curences a tag must have for it to show in the Trends Component e Trend Tag Whitelist Blacklist a list of tags to either be explicitly included whitelist or excluded blacklist from the trends components This can be used to filter the top hashtags returned into a more concise set Data Views Data within this component can be viewed in 3 different ways as a scatter graph as a bar chart or as a line graph These views
30. a concrete implementation of the Service interface providing the re quired functionality for the service A typical example of a Service interface is shown below import com google gwt user client rpc RemoteService import com google gwt user client rpc RemoteServiceRelativePath RemoteServiceRelativePath birdcall public interface TwitterQueryService extends RemoteService InitialResponse getChirps Request r This class imports the required classes extending RemoteService The function that the server side class is expected to implement is defined and a name is also given to the service birdcall which identifies the service within the application manifest The related ServiceAsync interface 1s shown below import com google gwt user client rpc AsyncCallback public interface TwitterQueryServiceAsync void getChirps Request r AsyncCallback lt InitialResponse gt callback Instantiations of the auto generated implementation of this interface are used client side to make service requests Importantly 1t must be supplied an asynchronous callback function which executes on return all services within GWT are non blocking given that the client application runs in a single threaded environment The callback handles the response from the server or failure if the service throws an exception These two classes plus their server side implementations are defined for each service used by Pulse The server imple
31. a viable option Furthermore users can specify various options configuring how much or little of daily activity they want to see The TemporalQueryManager applies these constraints and filters the results as requested Filtering options available to users that are applied by TemporalQueryManager include e Additional days the number of additional days either side of the query date to return For example a temporal range query with a date of January 20th and Additional days set to 10 will return data from January 10th to January 30th The default value is 10 e Daily Top Tag Limit the number of popular tags by total uses to return Whilst each file contains details of the top 0 25 of hashtags for that day this can be filtered further by setting a hard limit The default value 1s 50 e Retweets to Show the number of top retweets by total occurences to return The default value is 20 e Popular Links to Show the number of top links by total occurences to return The default value is 20 e Num Devices to Show the number of popular devices and corresponding usage volumes to return The default is 10 All other temporal range query options are applied by individual components client side If data is available to fulfil a request then the required DailyStats objects are processed The Tem poralQueryManager has several methods which extract and transform data from each DailyStats object so that it can be more easily transmitted and
32. and manager by indexing manager 13 Tweet date equal to or after Writer created tweets writ Success last munute s minute trigger ten to file tweets written to file Table 4 9 Tests Streaming missed at the merging stage Test case 2 describes the file in use error received when trying to delete the interim indices from the dailyIndices directory Interestingly only 1 file from each index was in use a Terrier index file that was somehow being held open by the application After checking to ensure all files were being closed after indexing the error still appeared Only by running the directory cleanup as a separate process did the error not occur For these reasons and others discussed previously daily merging was extracted and performed through a separate application N B As a primarily autonomous application little user input 1s required except on start up As such all filepaths if initially configured correctly will be valid through the various application stages streaming writing and indexing The nature of the application designed to be autonomously run for extended periods of time meant that testing was particularly challenging New features and were regularly added as the design evolved and ensuring that this had no impact on existing functionality meant not only feature 48 Test Case ID Description Tweet source folder files lt gt 60 EX EOF reached in minute file Multiple indexers running
33. applying style and structure to HTML elements Though most of the structural posi tioning of Pulse was performed using GWT panels individual elements were styled to maintain a system wide appearance A minimal colour palette of white turquoise and navy blue was chosen for the user interface providing a clean interface once the application loads 54 Components Component 2 ll 2 Q a T Management Framework a oO 0 k LL Application Framework Display view of results within component widget area Manage the component area creating updating amp clearing coms Struciure the application query bar nav bar component area Provide low level structure panels panes content display areas Provide analysis and manipulation functionality Request results from server using Service proxy Define RPC services Enable client Server communication through RPCs Support update with new result set Check results for integrity and parse results Handle user input Provide low level widgets form fields selectors Support update from cached result Set Manage the query cache Provide meta functionality comparso navigation cache requests Compile JavaScript to Java generate web archive Google Web Figure 5 1 Application Architecture 5 3 Architecture Figure 5 1 illustrates the client application architecture divided into four distinct layers Pu
34. are explained in detail in Appendix A 5 7 10 Place Device Component The Place Device Component shown in Figure 5 26 displays a graph of devices used to access Twitter over a specific time range as well as a map for each day in the time range highlighting country level tweet volume Purpose Surprisingly Twitter does not regularly provide detailed service usage statistics This component was designed to address the lack of data by visualising specific information about Twitter usage by country and by device 101 Switch to Places Switch to Devices Change Day Displayed Wer Plagg Device Usage Chart Daily Geoheatmap Figure 5 26 Place Device Component Data Requirements This component uses the country occurences hashmap and top devices hashmaps returned by the server in response to temporal range queries The country occurences hashmap contains string keys representing the Unix timestamp for each day returned and a comma separated string value with country occurrence pairs lt country gt lt numOccurences gt lt country gt lt numOccurences gt The top sources hashmap is structured in the same way with each hashmap value a comma separated list of devices lt device gt lt numUses gt lt device gt lt numUses gt Data Preparation amp Visualisation The component contains two views a line graph plotting device usage over time and a series of maps showing Twitter usa
35. are split by query type generally one set for tag based queries and another set for temporal range queries There are several options relevant only to specific components so each 1s discussed in detail within that component s description in Section 5 7 5 5 5 Component Area The component area initially empty 1s used to display all components for a given query The component manager controls the component area where during the update workflow components are generated updated and cleared 64 5 6 Layer 2 Component Management The core content units of Pulse components are managed by a component manager The compo nent manager is responsible for accepting queries requesting and parsing results creating compo nents and managing the process of updating each component It is also responsible for tracking storing and retrieving cached queries passing this information to components when appropriate 5 6 1 Purpose Using a component manager to control each component both in terms of structure and content has a number of advantages Primarily it allows for application modularisation and complexity masking each component is unaffected by the success or failure of other components 1s not required to handle any query input and is unconcerned with its position relative to other components It also provides a single location for components to access the validated response to a query Com ponents can safely assume that the data bein
36. as trivial or not fit for purpose Statement 7 The information such as help and other documentation provided with Pulse was clear had a surprising result Almost all users felt the documentation was appropriate the state ment had a median rank of 1 except the 2 most technically advanced participants This may be down to the attitudes displayed by these participants rather than completing every walkthrough they both skipped certain ones to explore the system This training stage was essential for other participants to fully understand the system s capabilities but advanced users may require a simpler method that both allows freedom and educates simultaneously A potential solution would be more elements with hover actions explaining the function of an element which can be quickly checked and either investigated or ignored depending on the user s usage requirements The aggregated results of questions 8 through 14 are shown in Figure 7 13 PostQ Usability Questionnaire Boxplot Q8 14 al Qq13 1 2 oi4 PTE E E TAA M Min i J A Median Max as D al as al MS X Quartiles Figure 7 13 Result Boxplots Questions 8 14 Statement 8 It was easy to find the information I needed focused on whether the paths to in formation were clearly signposted Participant agreement with this statement spread from strongly agree to neither agree nor disagree suggesting that some participants were
37. avoid with so many features but still need to declutter Redesigning the component area to shrink components not in active use would go some way to alleviating this issue and is considered a priority future development Verbal Protocol Analysis A quantitative analysis of each guided interview was also collated by performing a retrospective verbal protocol analysis 48 This involves coding each participants response during the guided interview as positive or negative to establish general sentiment towards their experience using Pulse Table 7 4 shows the results of this analysis for each participant as well as additional note worthy comments From the results it can be seen that the percentage of positive filtered words correlates almost exactly to the aggregate positive sentiment of the verbal protocal analysis both resulted in a score of 71 This is particularly remarkable given that each was based on entirely different measurements and elicitation methods The remaining percentage of negative remarks generally concerned the busyness or cluttered nature of the user interface once components were displayed These criticisms are understandable as Pulse is currently an early stage product Providing the required system functionality took precedence in development until late in the project With this functionality in place further development could focus on implementing changes suggested by participants in the user evaluation
38. concurrently A Terrier indexing fails for any reason Tweet file is corrupt Table 4 10 Test Cases Indexing Test Input Expected Output Result Fa a A Collection class supplied Change in value logged and Success file total adjusted Io oe fn 2 EOF reached in current Load new tweet file if any Success new file loaded if o ee 3 Multiple indexers initiated Indices of types specified Failure concurrent modifi Scenery ad Sion enor owt es 4 Terrier fails to complete in Retry attempt next index Partial attempts next index 5 Corrupted tweet file Ignore file and continue in Success file ignored and Table 4 11 Tests Indexing testing but also longer term integration testing This testing sometimes occurred over a number of days and occasionally errors in the code would only manifest themselves after a sustained period of operation For example the original trigger code that initiated writing and indexing would occasionally go out of synchronisation with the tweet stream The issue only manifested itself after a number of days running which meant during this time the integrity of the data streamed was unknown Only by monitoring logs gathered over time did a slow network connection reveal itself making the application go out of sync with tweets being streamed Although a minor fix the debugging process cost both time and data Similar to this situation were issues in the daily mergers of indices In that scen
39. dailyStatsTop10 is user mention data Pulse does not provide analysis of user activity so mention data only slowed down file loading at query time However future components could use this information to rank users and map social connections The smaller file also excludes 99 25 of all hashtags posted storing only the top 0 25 roughly 700 per day Usage of hashtags on Twitter follows a power law distribution whereby a very small percentage of hashtags are used very frequently each day with a long tail of infrequently used hashtags The ability to freely create hashtags by preprending to any word means there are limitless potential hashtags the vast majority of which never build momentum Those that do are stored for use in Pulse Retweets and sources are limited as well The number of devices used to tweet also exhibits a power law distribution from the vastly popular official apps down to the most esoteric handbuilt programs Of these only the top 35 are stored each day chosen as this number were found to be used 10 000 times or more per day Retweets are also limited to the top 20 by number of occurences The number of URLs stored is also vastly reduced by number of occurences 68 of all links posted occur only once and a further 21 occur less than five times For a link to be classified as popular Pulse requires at least 100 occurences in a single day this equates to roughly 0 1 of all links posted With the amount
40. details in Section 5 6 8 Each component is obliged to explicitly inform the compo nent manager when it has finished updating this is to avoid issues with asynchronous callbacks returning after the component manager has completed the update workflow Once each component has been updated the Query Bar search button 1s re enabled ready for the next query Each step in this workflow is explained in greater detail below 5 6 3 Initialisation Before Pulse can begin responding to queries various APIs and services must be initialised Internal Services Pulse relies on server side services to perform Java language manipulation and data retrieval which would be otherwise impossible on the client This is achieved using Remote Procedure Calls RPC between the client and server To facilitate this RPC proxy stubs are configured for each service and as these are reusable they are initialised on application startup by the component manager Services can then be invoked by calling the appropriate method on the RPC proxy stub Execution continues immediately after a service invocation so a callback method must be supplied to handle the server response The actual implementation of these services is on the server The implementation details are dis cussed in Chapter 6 External APIs Pulse uses several external APIs when responding to queries which are initialised by the component manager on application startup A dialog will show within the com
41. e Could Have Pivot data visualisations where a set of information can be pivoted on different vari ables to highlight specific properties of the data Caching performed by the client removing the need for users to make unnecessary query requests trying to search for the same data twice A mechanism allowing users to search for an individual user The results returned should give a complete picture of that user s Twitter activities social connections level of engagement influence and usage tendencies e Would Like to Have An mobile version of the application for use on small form factor devices N B These requirements apply to the system as a whole each directly related to the aims of the project Subsystems have their own internal requirements defined in their respective chapters 3 2 Core Technologies The requirements set out in Section 3 1 define what Pulse must be able to do These were used to select particular technologies which would most easily and quickly support building the required functionality Early in development it became clear that Pulse would require two main subsystems one for data gathering the data processing application and one for users allowing them to interact with the data the user application This section describes the core technologies evaluated and gives justification for the technologies selected 3 2 1 User Application Framework The most signific
42. ee 105 5 7 13 Popular Links Component 0 0 000002 eee 108 DOr COMCHISIOM 2x se oy ve she lt lt AP a e ir da ETA Sl r aa e E T 109 Pulse User Application Server Side 110 Onl Services OOVELVICW Sodi 2 Bet so Bee Oe 110 622 PUIDOSE de Be e eee RS eee we ee ee ee ee ee ES A 111 0 3 SaD OL Services lt lt a e ee a aa is Ee A T 111 7 OA Tweet Fetch Service 2 4 4 Ys te ei he Eee es Oe SSS ESS 112 CAL IREQUITCINCINS amp yu wd E a ee ee ee ee eS d 112 04 2 ATCC es S22 oe as oe a Ai A A AR 112 OAS ETOCS ca os AA 113 G44 PermormanceAnalysis 4 6 6 Bos Oe 8 Goh er SE Be 120 6 5 Sentiment Analysis Service ee 122 030 JDeplOyMent 4s 242 A AA o eee SA E A 122 07 COnCIUSIONS aan mace Beye ho oie E A ee A he me be hae Y 123 User Evaluation 124 Tole EValUa tons as IB A as Ook Ge ee es BESS 124 7 2 SEvaluation Preparduon e oidos E A AA A ke ele Oe Y 124 PRE Pre Pvaluiaon 2 6 2c amp oo OS Re ee ERE ds EYES 125 P22 EBNa lacio Lasko gt e a Boe a TE A ORK A T 125 P20 Post Evaluation REVIEW isa A a A we 127 Tele NN A A Bue Gene ede a dee ee oS amp oe ls Be 128 72 ParucipantRecruitment ao a wale a dA A SR 128 12 6 Eivaluation SessionS 22 44 2468 2644 04 04 64 8b64 2d A 129 7 3 Pre Evaluation Results Analysis 2 2 a 129 7 3 1 Social Networking Experience 0 0002 ee eee 129 7 3 2 Trend Analysis Experience 131 To IWI
43. ee a e BS 39 AS Pile Viana cementos bi ds bd e A eke A 35 AG ANEXAS lt lt a A Is eee eee ia e RRS 37 Aor Tadema ps AA O is ta Be ase UE ne 37 462 dndexneNManaver as 2 i redt boa ee der ee OE oe es 37 AO DateCleansme a ee e Be ee haw Ree ee ee Dee BES A 39 AO dE a 2 5 6 9 Goes Be A Eo Se i ee 2 39 AOS Daly Inde e 4 0 2064 4 6 eee hee EA be SRS 40 AF Palut RECOVeIy eds e E Bee bo Se A ee ee d 43 4 8 Adherence to API Usage Conditions a 43 49 Additional Batch Processie sos je ros gigans oi ee ee a wR ce e i 45 AO ASS INS gt 42 od fa Be eng Ge MMe de Seater ale A a Candee Boh dr a 46 A Ti Performance Analysi a a si 6 044400 4 4086 amp amp od ad eR eS 49 Aa CONCISO 2 6 eae E oS A O ew owe ee ee A AA 51 Pulse User Application Client Side 52 5 1 Application Requirements 0 0 2 eee eee 52 Skk User Requirements sie LEA a See OSS ase BESS 52 S12 System Requirements sara y Maw A dd wie a e ee Y 53 92 Desion Principles sece see a a RE A ds ees 54 A ACEC EOE se ot ae a E NE 55 Dee QUE PESA neea AE SR ee a a e ed BR eh A 57 Sas Hashtao Querics nula Sues eS ae ad ee ee BS eS 57 5 4 2 Slasha QGETICS 3028 E LAO A ar dA SS a 57 5 4 3 Temporal Range Queries ooa 6404 24one 824 58 5344 Cached Queries msc a a ea aa 58 5 5 Layer 1 Application Framework 00 eee ee ee 59 Sl GW ESEC S e aaa a Gad a a BE n a o 59 532 Application Ur Structure se e a e he Be 60 DR QUEDA a S
44. every query type has some subset of options which apply The SingleRequest and DuoRequest classes are specific to queries which feature a hashtag Each adds fields for hashtags one in the case of SingleRequest and two in the case of DuoRequest These are used by hashtag and slashtag queries respectively Responses are generated by the server after processing a request Different response are issued depending on the request type further discussion of responses can be found in Chapter 6 After each component has finished updating requests and responses are added to to the query cache If a slashtag request was made it is split into two hashtag requests and stored as two separate cache entries This means each query tag in a slashtag query can later be compared against any other query in the cache rather than just the tag specified in the initial slashtag request The query cache is explained fully in Section 5 6 6 5 6 5 Component amp Request Types All components have a component type which 1s included within one or more request types Both component and request types are defined as Java enumerations components in ComponentType and requests within RequestType RequestType also has a method which returns the types of components required for a given type of request This is used by the NewComponentManager when generating components in response to a query 5 6 6 Query Cache Each hashtag or slashtag query request and response is stor
45. give insight into events in the Twittersphere on a given day or topic Spam or accounts which abuse the platform are likely to bias Pulse results as much as quality filtering on the part of Twitter 4 5 2 Connection Implementation Pulse utilises the Apache HTTPClient library 13 to create and configure HTTP connections to the Streaming API HTTPClient was selected primarily on Twitter recommendation it provides the full suite of functionality required for Twitter connections keep alive headers authentication status code information as well as allowing connections by proxy required by the university network It is also a lightweight mature and well documented library which helped quickly develop a working prototype of the application The Streaming API connection is formed initiated and closed in four stages 1 Configuration Appropriate parameters are set for the HTTP request and authentication de tails are added If the application is behind a proxy proxy settings are also configured 34 2 Request Response The HTTP request is executed and a response is returned If the response is OK 200 then streaming begins If not the application waits for an appropriate length of time 3 Stream Once a valid connection is established an InputStream and BufferedReader are cre ated These are used to read the response from Twitter and obtain each JSON entity 4 Closure Reconnect Once the reader returns null the connectio
46. hashtag index is loaded from disk then searched for all occurences of the query tag If there are any results the Terrier documents IDs returned are used to obtain tweet UIDs from the Terrier meta index 114 2 If there are any results from the first search the UID lookup index is loaded Each tweet UID is added to a query string which is then supplied to Terrier for querying The UID lookup meta index is then used to obtain the full JSON tweet string Stage 1 is illustrated in Figure 6 4 Get tweet UIDs Execute Terrier from meta index query by Terrier docid in result set Reuse loaded index queries much Filter tweet UIDs Retum array of tweet based on options UIDs retrieved i Creates new search request Create new sels parameters runs chain query faster TerrierSearch with of filters ore orec match correct hashtag posi proc post lllers index Loads index from file path creates query manager sets Terrier properties no termolpelines atc Figure 6 4 TagQueryManager Hashtag Lookup The TerrierSearch class based on the InteractiveQuerying 36 class provided with Terrier to demonstrate command line search 1s responsible for the end to end Terrier querying process The TagQueryManager creates a two instances on initialisation one each for hashtag indices and UID lookup indices These are wholly responsible for e Loading the required Terrier index file the path provided by the tag query
47. not fully satisfied with the information finding process Once again this is likely an effort in user education through im plicit or explicit means Many users took only a cursory glance the user guide throughout the whole evaluation as with many other of these statements online help would significantly aid users in information finding Statement 9 The information provided by Pulse was easy to understand assesses the meaningful ness of results returned Most participants expressed agreement with this statement however verbal feedback from many sessions was that the Trends Component was initially quite daunting and not very well explained The user guide provides a full breakdown of this component however adding a short animation to cycle through each option view and mode the first time the component loads would aid users in fully comprehending the analysis possible by using it correctly Statement 10 The information was effective in helping me complete the set tasks had strong agree ment from almost all participants Statement 11 The organisation of information within Pulse is clear was important in verifying that 140 the component centric layout worked from a user perspective This statement had a median value of 3 weak agreement Verbal feedback from participants highlighted that the lack of strong agree ment wasn t about the layout of components but more the placing of additional options within com plex components for e
48. processed by the client The extraction and transformation process is shown below 118 for each data type tags retweets links etc dateTimestamp queryDate toString dataHashmap new Hashmap stores daily data for this data type for each day before query date offset 86400000 1 day in ms newTimestamp queryDate toLong offset statsObject cache get newTimestamp stringRepStatsObject parse statsObject see below dataHashmap put newTimestamp stringRepStatsObject offset 0 for each day after query date offset 86400000 newTimestamp queryDate toLongtoffset statsObject cache get newTimestamp stringRepStatsObject parse statsObject see below dataHashmap put newTimestamp stringRepStatsObject return dataHashmap CONSEEUCL Tesponse The data types extracted are shown in Figure 6 7 The parsing required to create a string representation of each data type varies greatly from type to type For example hashtags are stored as HashtagInfo objects within the DailyStats file so these need to be manipulated differently from retweets which are stored as RetweetInfo objects As a rule the data for each type is transformed into a string where each unit of data a single tag or retweet is comma separated and the attributes of each data unit number of tag occurrences number of unique tag users etc 1s space separated Every data type in the DailyStats object has an associated parsing method which
49. scenario However once the main scenarios have been completed users are invited to undertake system exploration Put simply participants were encouraged to query for any topic or period of interest interacting with components freely Whilst difficult to analyse quantatively the aim was to see how users with experience would use the system given no direction The final evaluation task list also included a short preamble explaining the evaluation procedure points which the participant should consider as they completed the evaluation and standard infor mation concerning evaluation etiquette 126 7 2 3 Post Evaluation Review Typically user evaluations are conducted to establish the usability of a system Usability defined as the extent to which a product can be used by specified users to achieve specified goals with effectiveness efficiency and satisfaction in a specified context of use 51 is only one part of the user experience Though an important aspect of system evaluation it is primarily focused on assessing the ease in which users can achieve certain goals As users turn to technology for entertainment and general interest the desirability of a system must also be considered Desirability defined as Worth having or seeking as by being useful advanta geous or pleasing 44 concerns intangible qualitative terms which can be applied to products fun engaging boring useful complex overwhelming and colourful
50. short time to respond Nielsen explains that after 10 seconds users being to lose focus on the task at hand if no response or feedback is given 55 Queries are issued in real time by users so the delay between clicking Search and seeing results should be as small as possible This is the main performance factor on which the service will be tested 6 4 2 Architecture The Tweet Fetch service architecture is illustrated in Figure 6 1 Requests from the client are initially received by the TwitterQueryServicelmpl class This is an implementation of the service interfaces defined by the application framework and also acts as the service entry point Requests are then delegated to the appropriate query manager TagQuery Manager for hashtag based query requests and TemporalQueryManager for temporal range query requests Each manager is responsible for the end to end processing of particular query types The Tag QueryManager operates a pipeline retrieving tweets based on query tags whereas the Temporal Query Manager retrieves daily statistics based on a query date 112 TwitterQueryServicelmpl service entry point TagQueryManager handle all tag queries TerrierSearch TerrierSearch hashtags tweet UlDs queries hashtag index for quenes UID lookup tweet U Ds index for JSON tweets Pulse Data Store Figure 6 1 Tweet Fetch Service Architecture The TagQueryManager needs to interact with Terrier indices to get twee
51. show the results for that set and handle zero results for the other set gracefully Test Input Expected Output Result ID 1 Slashtag query returns 2 re If only 1 result set is anal Success component dis sult sets one large one ysed display within single plays only 1 tab panel small and sentiment analy tab panel sis has low value Table 5 20 Tests Sentiment Analysis Duo Component Future Development Currently the component will try and analyse any tweet returned from each query which includes non English tweets There were several ways to approach this problem each with advantages and disadvantages The first approach is by far the simplest ignoring tweets where the lang atrribute is not en This key included in every tweet entity as an indicator of the user s native language What it does not indicate is the language of the tweet text Table 5 21 shows the accuracy level of this information for a small set of 100 tweets obtained after searching for the tag news Tweets Classified Language Match Language Mismatch 100 Table 5 21 Language Classification lang attribute As shown the level of accuracy is fairly poor 24 of tweets sampled were misidentified as English The second approach tried was to detect if any non ASCII characters had been used in the tweet limiting the allowable alphabet to Latin characters only The same sample of 100 tweets was checked to see how accurate t
52. spent investigating the root cause However the testing conducted revealed that the issue was impossible to recreate on a non university machine Based on this and considering the regularity and predictability of the dropped connections I believe it was caused by the university proxy server resetting the connection after an hour Whilst not ideal the limited effect on data quality meant this was an acceptable cost given the benefits derived from being able to stream tweets for almost 24 hours a day 4 5 5 File Management The organisation and structure of files 1s defined by key time periods on startup every day every hour and every minute When the specific trigger points for each of these periods is reached one or more actions are taken by the application Figure 4 6 gives an overview of these events 35 Create new daily directory with iweets and indioes Create index manager and merge all yesterday s hourly indicas Create new hourly directory for tweets and indices Create index manager and index last hours tweets Write tweets in waiting list to new minute file of current hour Add tweet to waiting list Figure 4 6 File management logic On startup the application performs the failure recovery checks detailed in Section 4 7 However Should the current instance be a fresh run Pulse will create a series of directories in which to store tweet and index data It will also configure all of the trigger points of
53. still a positive result of 3 Statement 4 I felt comfortable using Pulse was important for measuring how quickly partic pants felt at ease with the system The user interface was designed to be as unintimidating as possible at startup with the query bar inviting users to begin searching This worked to a large extent most users scored 2 for this statement suggesting they were at ease operating the system Statement 5 It was easy to learn how to use Pulse was expected to yield low scores given the large number of features and the limited time available within the evaluation It is testament to the clarity of the application that most users scored this statement 2 or higher with only the most novice users requiring verbal assistance during the evaluation Once again additional online help would almost certainly result in this score rising Statement 6 J believe I became productive quickly using Pulse had an interesting spread of results Whilst 4 participants ranked this 2 or higher the remaining 3 all selected option 4 neither agreeing not disagreeing As a web based application Pulse should enable users to immediately find what they require if they have a task in mind so adding help that would improve the rank of this statement would be a future development priority That said Pulse does balance a fine line between 139 immediacy and depth and oversimplification of the system could result in users dismissing it
54. the left of the play button to change playback speed 10 Enlarge Trends Component The Trends Component contains much detail In order to better use it click this button for the component to occupy all space in the component area 11 Size Variable The size of each node can be used to represent a fourth variable Available options are Same Size Countries Unique Users and Occurences 12 Use Trails Show trails as each node Is animated Use in conjunction with the time slider to create ad hoc line graphs 13 Chart Area Area used to display results Using the Trends Component The Trends Component has a number of options and variables which you can manipulate to view data several ways The three main views of this component are e Scatter View each tag is displayed as a node in the graph area You can change the X and Y axises as well as the size and colour of each node to represent up to four variables number of unique users using the tag number of occurences number of countries using tag and time tag used Scatter view is shown in the diagram on the previous page 27 e Bar View each tag is displayed as a single bar in the graph area Again you can change the variables assigned to either axis the colour or size of each bar Bar View is shown below cropped for space PP Order Unique Users jajajajajajajaja ja 11 02 2011 e Line View each tag is displayed as a
55. the application minutely hourly and at midnight Once streaming begins directories for the current hour will also be created if they do not already exist Every minute a new file is created within the current hour directory of the current day This file contains the combined tweets for the previous minute and is named using that minute s Unix times tamp e g gzipTweets lt UnixTimestamp gt Tweets are stored in compressed GZIP files as binary objects Each hourly folder usually contains 60 of these files corresponding to one per minute of streaming However this can vary if the connection is slowed or dropped for any significant period of time gt 1 minute Tweets were originally stored in hourly files however the volume of data kept in memory meant that this option quickly became unfeasible Electing to divide tweets into files by minute eradicates this issue It also adds robustness to the system in that if for any reason a writer thread fails to complete writing only one minute s worth of tweets 1s lost Every hour new directories are created for that hour s tweets and indices Like the daily directories and the minute files these are named using the Unix timestamp of the hour the represent e g from lt UnixTimestamp gt 60mins Every day a new daily data directory is created along with corresponding tweets and indices folders 36 4 6 Indexing 4 6 1 Indexing Triggers Tweets are indexed usin
56. the tests in Table 5 4 T1 A hashtag query with re Component displays query Success component dis sults tag at top of component plays query tag volumes volume in brackets associ and tag cloud ated tags with volumes as a cloud A hashtag query with re Component displays query Success component dis sults none of which have tag at top of component plays query tag alone any other hashtags apart along with volume in rom the query tag brackets A slashtag query where Output from one of test Success output is one of A cached hashtag query Quick display of output Success data loaded from from test cases 1 3 cache and displayed within component A click on any tag within Tag is sent to search box Success tag sent to search the result cloud and Go button is progra box and button clicked ini matically clicked tiating new query Hover mouse over any tag Tag colour changes to white Success on hover tag turns within the result cloud until hover out white Table 5 2 Tests Tagged With Component Spit Tag Cloud Options List Split Tag Cloud MA Split Tag Cloud Tag Comparison Details Cloud Divider Figure 5 19 Tagged With Duo Component 5 7 4 Location Component The Location Component illustrated in Figure 5 20 marks each tweet on a world map using longi tude latitude data As very few tweets provide this natively the Google Maps API initialised by the componen
57. two tabs of basic statistics one for each tag Comparing Multiple Queries Any hashtag or slashtag query can be compared against any other in the Compare Panel of the Navigation Bar For example searching apple google then ps3 xbox will still allow google xbox to be compared from cache each slashtag query result is stored independently of the other tag 22 Querying Pulse Time As well as querying Pulse using topic based queries you can also search solely based on time periods Time Queries Time queries present results such as trends popular terms or device usage from across a day week or even month To make a time query 1 Clear the Query Box in the Query Bar This signifies a time search rather than a topic search of a given time 2 Select a date to query using the Date Picker Note you cannot type directly into the Date Picker Use the calendar that appears on clicking the Date Picker to select a date Tue Feb 15 2011 3 Optional set options for your Query within the Options Panel of the Navigation Bar see below 4 Click the Search Button in the Query Bar Time Query Options Various options can be set for time queries within the Options Panel under Pulse Queries Pulse Queries Additional Days Information Daily Top Tags Lim 50 Daily Top Tag Limit Retweet Display Limit Tag Minimum Trend Lower Lim 0 Trend Upper Lim 0 Maximum Occurences Retwee
58. walkthroughs and by utilising the user manual No guidelines are given on how this should be achieved rather the user must explore Pulse and find the required information in whatever way they deem most appropriate For every scenario there are a number of methods which can be used to obtain the required data however there is always one method which is simplest Users then needed to be observed to understand whether the easiest method was chosen and if not why not It could be because it was not immediately obvious difficult to access or simply obfuscated Should any of these be true it would have a measurable impact on user satisfaction Scenarios were also used to showcase the variety of components in Pulse Each scenario focuses on one or more specific components providing participants with further learning opportunities and experience to reflect on once the evaluation was complete Ensuring the scenarios were varied was also crucial in maintaining user interest and establishing which components were most useful and engaging The scenarios selected for the evaluation are shown in Table 7 1 2 Hashtag Query Tag Volume 3 Comparison Date Tagged With Tag Volume 4 Shashiag Query Geographical Heatmap 8 Time Query Options Trends o Time Query Options Place Device Table 7 1 User Evaluation Scenarios Given the time constraints of the evaluation it was unfeasible to include every component for each
59. was desirable Usability was evaluated through a set of formal evaluation methods Timings to complete each task were taken users were asked to think aloud during the course of the evaluation and were issued with a usability questionnaire to rate their experience Desirability was assessed by encouraging participants to describe their experience using Pulse by selecting words from a wide selection of adjectives Amongst the words chosen regularly were useful engaging and advanced but perhaps most satisfying for a data analysis application was the frequent use of the word fun In both cases users overwhelmingly agreed that Pulse was both usable and desirable However as well as giving praise users were also honest in their feedback when the system failed to operate as they expected This invaluable data is discussed further in Section 8 2 In summary Pulse manages to achieve the aims defined on undertaking the project A thorough analysis development and testing process ensured Pulse contained a comprehensive suite of func tionality Several potential users validated its usability and desirability Finally reviewing the requirements confirms that Pulse is fit for relevant fit for purpose and capable 8 2 Future Development The size of the Twitter domain and the virtually endless data manipulation possibilities that exist means that there 1s significant scope for Pulse to be extended and improved As it stan
60. will apply to both hashtags specified in the slashtag query See Hashtag Query Options for specific details of how these work Slashtag Query Results Once the slashtag query has completed there are three possible outcomes 1 Both queries return results These will be displayed within the component area as described below 2 Only one query returns any results In this case an error message will be shown and the tag that did have results will be displayed as if it were a hashtag query Pulse Only wikileaks returned any results Switching to single hashtag mode Close 3 Neither hashtag returns any results In this case an error message will be shown Pulse Neither search term returned any results Close Slashtag Query Components Successful slashtag queries will result in six Duo components being displayed inside the Component Area as well as both queries being cached Note these components will also be used when comparing two existing queries from the query cache should both queries have results 19 0 009 oo oo oo eseese 555 3 GEER F888 000000 00 00 00 ecce 00000 O00 00 00 00000 Sseese ccocoo ocooso o Location Duo Component 9000 00000 00000 Bape w aa Tag Volume Duo Component EA P Tagged V With Duo Geographical N In Context Duo Heatmap Duo Component Component Tagged With
61. 00 detail in the relevant sections of this user guide However for all options Se Saale as er Hover over an option s description to see a tooltip and find out more about what it does The perce default 100 Or hover over the option s input field to see a tooltip of typical or allowed values for that option A percentage between 0 and 100 Invalid input within options will be replaced by that option s default values Component Area Use POS Parser M Plotting 100 Pulse Queries Additional Days 10 Daily Top Tags Lim 50 Trend Lower Lim 0 Trend Upper Lim 0 Retweets To Show 20 Popular Links To Show 20 Num Devices To Show 10 Exclude web from Devices Y Trend Tag Whitelist Clear Trend Tag Blacklist Clear History Options Clear Cache The Component Area is where all of the components generated for each query are displayed This varies dramatically for different types of query so the Component Area is discussed in more detail in the Querying Pulse section of this guide 11 Querying Pulse Topics Pulse provides a wealth of information statistics and analysis of tweets over a given period or for a particular topic hashtag This is presented using components self contained units which display information on a particular facet of search results These components differ depending on the type of query issued
62. 9 Slashtag Query Results 0 esessssssssssssssesesscsssssessssesssessssesesssessesesssessssessssessesssssessesesesesscseasesessessassessess 19 Slashtag Query Components esessssssessssssssesessssesesssesscseseseseesesssssessesesssussesessessesseseassesseseasssesseseaesesseseess 19 Tagged With Duo COMPOR INT cee vec 02 soe secceer telcos este eae eee eee 20 Location DUO C OMPONG tesis ia tre trite frm tere re rte re tere ree tree tree ere ree ee rere 21 Tag Volume Duo COMPONEN Casassnanapaasanannr e 21 Sentiment Analysis Duo COMPONENE seusnsiososoaniosislooionpiolalo sio uonios lie 21 Geographical Heatmap DUO COMPONEN ee eessssssessesesssesessesssssessesessseseesessseseeseseaesesesesssesesseseess 22 Mi Comex DUO COMPONEN Esenciales 22 Comparing Multiple QUEMIES ee esesesssessesesesesesssesesessssesesesessesesesessesesesussesesesnscssesesecsseseseensesseseaeeeeseaeens 22 QUESTION RUSS MANS sr se eii 23 TASS UTC Ss ccarnsnrarrrarir naa cara iia ae 23 Time Query OD COINS isinciiniaratiaa ia 23 We Quen RESUS anina los 25 NE UR Ty COMPONEN Storia 25 Trends COMPONEN errata erT EA E ARANAS TAES 26 Place Device COMPONE NK ee eeeesccsesesssssesssssssesssscsesesesesessssssssesesesesssesussesssesesesesesesssssseseseseseaesesessesesesees 29 Sentiment Volume Trends COMPONEN A csesessssesesesesesesssssscsesesesesescsesscsesesesesesesesesseseseseseaees 30 Retweet Paths COMODO CMC aaa 31 Popular LINKS COMPONENL cceccesecsee
63. A Q1 Used Trend Tools E Used trend analysis tools Figure 7 4 Trend Analysis Tools Used Interestingly over half the the evaluation group had some trend analysis tools experience Whilst this was positive in that these users could be expected to understand Pulse capabilities faster this is a much larger proportion than one would expect in typical user group of the same age from outside of an academic setting and has the potential to bias the results PreQ TA Q2 Usage Reasons PreQ TA Q4 Tools Used Market Academic Academic Own Not Used research Computing Other Interest Figure 7 5 Reasons for Use and Tools Used Figure 7 5 a illustrates reasons participants had used trend analysis tools whilst Figure 7 5 b shows the tools used Of those participants with analytical experience one had previously worked as an analyst at a social media marketing business When asked what features were most useful in tools they had used it was the ability to drill down on metrics for granular data Most however had only used Twitter Search a useful feature of which being the dynamically updated results page or none at all 131 7 3 3 Twitter Experience The Twitter usage rate of each participant was also investigated Pre T Q1 Have Account PreQ T Q2 Twitter Usage Figure 7 6 Users with Twitter Accounts Frequency of Account Use Figure 7 6 a highlights those with Twitter accounts and Figure 7 6 b shows how often th
64. API to obtain tweets This API allows connecting applications to utilise an HTTP connection to stream tweets in real time as posted by users this process is described further in Section 4 5 29 pulseHomeDataDir dayDataDir from lt HrTimestamp gt lt HrTimestamp gt Dir lt minutel imestamp gt GZIP fail indexF ile mdexFile INDEX le uidLookup ak 10 allDaylndicesDir hashtags J fog txt aoe van 100 dayDataDir dayDataDir Laa N Figure 4 4 Data folder tree structure Twitter has three access levels designated for the Streaming API Firehose all public tweets Gardenhose around 10 of all public tweets and Sprinkler around 1 of all public tweets For this project a request for Gardenhose access was granted by Twitter This is the typical access level required for meaningful data analysis with an average of 10 million tweets per day the Gardenhose access level provides a significant data set to study Due to storage constraints Pulse further reduces the volume tweets provided by the Streaming API to arandom sample of 50 of the Gardenhose The final volume stored by Pulse is roughly 5 of all public tweets Once read from the input stream tweets are parsed and stored as discussed in Section 4 5 4 4 Tweet Entities 4 4 1 Data Format An individual tweet as streamed to Pulse can be requested as either a Javascript Object Notation JSON 27 object o
65. Adding new components to display information in a different way should be possible for advanced users Similarly existing components should be organised in such a way that modifying their functionality 1s trivial 1 4 Dissertation Outline The rest of this document describes the development process of Pulse It is structured as follows e Chapter 2 explains the purpose of the system as well as the aims to be achieved in creating Pulse e Chapter 3 describes the application requirements expected functionality and desirable fea tures as well as explaining the overall design of the toolkit describing the core technologies utilised and the application architecture e Chapter 4 discusses the data processing system powering Pulse 10 Chapter 5 describes the client side aspect of the Pulse user application including a detailed description of each component Chapter 6 describes the server side aspect of the Pulse user application Chapter 7 explains the rationale process and results of the Pulse system user evaluation Chapter 8 summarises the successes and challenges of the project as well as describing future development opportunities 11 Chapter 2 Background 2 1 Twitter A Microblogging Service The primary data source of Pulse is Twitter Twitter is a microblogging platform that allows users to post small updates tweets typically answering the question what s happening Within each tweet users can pos
66. Anatomy of Services http code google com webtoolkit doc 2 1 images AnatomyOfServices png March 2011 GWT Documentation Communicating with a Server http code google com webtoolkit doc 1 6 DevGuideServerCommunication html March 2011 Java 2 Platform EE v1 3 Class HttpServlet http download oracle com Javaee 1 3 api javax servlet http HttpServlet html March 2011 Java Servlet 2 5 Specification http jcp org aboutJava communityprocess mrel 3jsr154 index2 html March 2011 25 jQuery http jquery com March 2011 26 jQuery Usage Statistics http trends builtwith com javascript JQuery 27 28 29 30 31 32 33 34 35 36 37 March 2011 JSON org http www json org March 2011 LRUMap Javadoc Apache Commons Collection Library http commons apache org collections apidocs org apache commons collections map LRUMap html1 March 2011 MooTools a compact JavaScript framework http mootools net March 2011 MozillaZine Unresponsive Script Warning Mero mori dilazines oO Unresponsive_Script_Warning March 2011 RemoteServiceServlet Google Web Toolkit Javadoc hteeps 7 google web toolkit googlecode com svn javadoc 1 6 com google gwt user server rpc RemoteServiceServlet html March 2011 Skyttle Market Sentinel http www marketsentinel com tools skyttle March 2011 Terrier 3 0 Documentation Query Language http terrier org docs v3 0 query
67. Apache Tomcat 7 running with a 2Gb heap size on a quad core Windows PC e Index Size the tag query indices used were 254Mb hashtags and 4 8Gb UID lookup Tag Based Queries Timing values were taken for each of the following operations 1 Client Server transmission 2 Hashtag index tweet UID lookup 3 UID index JSON tweet lookup Meta index data retrieval Parsing Nn Nn A Server client transmission Certain operations such as trivial checks and tasks found to have a negligible time footprint have been excluded for clarity Additionally the time taken to initialise the service was not included this happens only once for as long as the application is running even between multiple browser sessions and users Table 6 1 shows the different scenarios used for testing Table 6 2 shows timings taken for each operation all times are in milliseconds Queries with a smaller number of results such as in scenarios 1 2 and 5 or those where indices have already been loaded such as scenarios 4 and 6 did return within the time limit aim of 10 seconds As tag usage frequencies follow a power law distribution this should be the case for the majority of hashtags in the data store However those with over 1000 results particularly from a non cached index return after a much longer delay than the suggested responsiveness limit 120 6 Slashtag Query TRUE BA 8 Slashtag Query TRUE 1022 1061 T
68. Component see the most popular links posted to Twitter This is just a brief overview of the functionality and options within Pulse The rest of this user guide explains the key features and modes of the system in greater detail Pulse Tips Use the Search Box Efficiently You can enter several different types of query in the main search box e Prefix your query with a for a hashtag search o eg science e Add a slash between 2 hashtag queries to compare them simultaneously o eg Science art e Leave the query box empty and see the top results across the whole day selected Don t Search Twice Have you completed a search in the past whose results you d like to see again Select the search from the History tab in the navigation bar The results will load instantly and you can compare them with any other search See the Compare Tags Panel for more details Use Options to Narrow Search Pulse features many options to suit all types of queries Don t need full map plotting Turn it off Want more accuracy in sentiment analysis Use the part of speech tagger Have a particular set of topics you d like to analyse over a day Set up a tag whitelist You can see all of these options within the Options Panel in the Navigation Bar Pulse Basics Startup Pulse requires an internet connection to start up Before using it please ensure you are connected to the internet The below message will show until an internet connectio
69. EN University 2A School of SJ of Glasgow Computing Science Pulse A Social Media Analysis Toolkit Paul Holmes School of Computing Science Sir Alwyn Williams Building University of Glasgow G12 8QQ Level 4 Project March 25th 2011 Abstract An explosion in the usage of social media applications such as Twitter has created a wealth of data well suited to mining and analysis With the correct tools this data can be analysed and interpreted in a meaningful way identifying trends establishing opinions and understanding usage This dissertation presents Pulse a social media analysis toolkit It is capable of processing tweets streamed directly from Twitter managing their storage and indexing analysing each for aggregated statistics and presenting the results in a browser based application Users can search using temporal or subject based queries and are presented with components that display information relevant to a particular time country or subject The results themselves are presented through a number of means as text graphically utilising chart visualisations and through use of maps Many social analysis tools already exist as desktop applications and websites However few pro vide such comprehensive analysis Existing tools are limited by the volume of data they utilise the domains in which they operate and the features which they implement Pulse encapsulates millions of tweet per day as subject trends geogra
70. Lin ema 08 02 2011 qe A ES Fa eE ra A A A O EA Daily Sentiment of All Tweets 1 003 E e E a RT scooterbruan RT you love justinbieber 202 200 gt i a RT tawon02 POM A WS SS SAS tip ool IADX2 Bola RSFS AR AAYAR Hoicive USSeP sea 08 200 Ber Positive 9 A 0729 o1e Se ZO Popular 409 400 Be Stignhy tesgalve RT harukazechan USAF ADOLECE BRUNO RIM NT SAS Mi Hio oeratet egatve CH MOLCVSAMREOWLSUE w BALCWMETEBA i 2a ven Negative B SEASECEADACTEELUUDES EL eves WES Links ae E e Pa E S S E esinsane WTF Garota de 18 anos morre logo ap s dar o 4 a Col po ne nt p gt dl z m Fd S S f W E 1 ro belo ho migre me 3RLor A stas rn dere Sentiment Volume Retweet Trends Component Paths Component 25 Trends Component The Trends Component is a multifunction pivot chart which allows for analysis of daily trend 4 Color Variable data in a number of formats comparing multiple variables Colour 13 Chart A chen tyeeats Countries 60 1 Change Y Axis Variable 40 2 Change Scale Linear Logarithmic Select _ 1day __ 25bahman _ 900daysofkisses _ Jahoetshirtwouldsay _Jalgeria _Jaristegui _ astonsbirthday Unique Users un o7 5 un 30 0 00 35 0 00 40 0 00 6 Change Scale Linear Logarithmic 9 Play Chart as Animation 7 Change X Axis Variable select _ 1day 8 Time Slider aristegui astonsbirthd
71. Tag Volume component Sentiment Analysis Duo Component The Sentiment Analysis Duo Component presents the results of sentiment analysis across two tab panels These can be changed using the options list in the upper right corner of the component Tweet Sentiment towards apple Options List Tag 1 Sentiment Analysis Tag 2 I Very Positive WB Moderately Positive Slightly Positive Wi Slightly Negative Wi Moderately Negative Sentiment Analysis WB Very Negative 21 Geographical Heatmap Duo Component The Duo version of the Geographical Heatmap Duo Component Heatmap for apple 7 provides maps for each query as well as a comparison map n Heatmap for apple Heatmap for google Comparison that shows the most popular tag per country You can select the map to view using the Options List right The comparison map below highlights the most popular tag per country By hovering on a particular country a window appears with further details which tag was more popular and by how much It also shows the total number of tweets for both tags in that country Comparison E Hover Information Window US apple 23 0 US apple 23 o AAA Total Tweets 38 Total Tweets 1 In Context Duo Component Like the Tag Volume Duo Component this component shows both queries scattergraph points as different colours for comparison Additionally there are
72. Typical JSON tweet entity The entity itself 1s a JSON object within which are multiple key value pairs These pairs can hold string and numerical values as well as additional nested JSON objects There are also JSON arrays that provide easy access to important entities of the tweet notably hashtags used URLs featured and users mentioned These can then be easily extracted and manipulated as required 4 4 3 Tweet Parsing Though tweets arrive as JSON entities from the Twitter stream they are immediately parsed using a JSON parser into objects of type Tweet The Tweet class gives convenient access to the oft used key value pairs stored within each tweet entity as well as convenience methods used by other Pulse components for processing manipulation and presentation of Twitter data The JSON parser used by Pulse is built upon a Java implementation of the reference parser available from json org 27 This was chosen early in the project based on previous experience parsing JSON Early benchmarks of parsing performance on incoming tweets at double the current rate showed no lag or backlog being caused by the parser Based on this it was selected for use in Pulse However were the application to process considerably more tweets per second this may have to be reviewed The JSON tweet entity itself is also stored as part of the Tweet object This has advantages in allowing future components to access currently unused values within the twee
73. a number of emoticons and applied a sentiment score based on which emoticons were used 92 This technique had numerous benefits Computationally it is extremely simple and could even be performed by the client application without any server calls Additionally it is accurate no actual classification is performed by the application as the user has already imbued sentiment through the use of an emoticon However there are several issues with this technique Firstly the variety of emoticons used by people is huge and new ones are created all the time Furthermore certain countries use very particular emoticons which deviate from the standard horizontal style common in the West for example Japanese emoticons are vertically orientated and considerably more expressive 60 such as _ expressing happiness Finally only a subset of all tweets feature emoticons of any kind Any that don t would be ignored by the component resulting in a greatly reduced sample size The next approach considered was OpinionFinder 59 a system of utilities and tools which can be be used to identify opinions within text However organising the pipeline of components required for analysis to perform in real time was a laborious task At the time of writing a complete Java re write of OpinionFinder is being undertaken to encapsulate the setup and configuration process as a single application In the mean time the system as is was deemed overly com
74. able 6 1 Tag Based Scenarios Cli Serv Trans HT index lookup UID index lookup Serv Cli Trans 6e as a ss 6e o os 7 wu 23858 ap gt o f o 600 2s eo x 9 7y 8 22 178 ES 2153 5116 a sr 210 39 m 5917 34117 40249 sl 8 38 1972 20 166 20309 Table 6 2 Tag Based Query Performance This is mainly due to the process of querying the UID lookup index then retrieving the relevant meta data for each tweet UID As discussed earlier Terrier does offer reverse key lookup for ob taining document IDs using a unique meta data value but this caused issues at index merging stage described in Section 4 6 5 Indices featuring reverse lookup keys were not available for testing however given future development time resolving the index merging reverse key issue would be a high priority development task Query responsiveness could also be improved hy further harnessing the benefits of caching extend ing the cache to include a larger number of data ranges One potential way of achieving this would be to implement an LRUMap 28 The LRU Least Recently Used Map is part of the Apache Com mons Collection library 11 providing a map implementation with a fixed number of entries Once the map has been filled to capacity adding any new entries automatically removes the least recently used entry Currently the tag query manager loads a new index if the current query date is different from the previous qu
75. able positive and acquiesence biases inherent in almost all forms of questionnaire Usability Analysis With a method for assessing desirability in place standard means were used to assess application usability An oft used quantitative method was utilised developed by IBM 53 which incorporates a series of statements and associated Likert scales on which participants can state their level of agreement 127 The scale used ranges from 1 Strongly Agree to 7 Strongly Disagree with each statement focusing on a particular aspect of usability ease of learning productivity system help clarity of information and so forth Though the IBM evaluation contains 19 statements several of these were better ranked through the desirability analysis For conciseness these were removed leaving 14 statements for participants to rate Though this questionnaire is subject to both positivity and acquiescence bias it still serves a useful purpose giving quantitative data which can be compared to the qualitative results of the desirability analysis to establish whether there are significantly different conclusions that can be drawn from each result set and if so what those differences are 7 2 4 Materials The following materials were developed for the user evaluation e User Guide a comprehensive user focused document explaining the features available as well as a Quick Start guide allowing users to immediately become productive
76. also be able to analyse as many tweets as possible e Usefulness the level of analysis must not be as broad as to be meaningless but should provide users with an accessible and comprehensive overview of sentiment towards a tag on a given day Furthermore it should justify results with samples and statistics In addition to these factors the challenges described in categorising tweets by Bifet et al 45 were also considered e Categorisation having a human operator categorise each tweet would be a laborious task with over 6 million tweets each day this becomes virtually impossible e Time the volume of tweets returned in each result set means the analysis process must be fast enough so that results are displayed in real time e Text Content text posted on Twitter may contain irony sarcasm or other products of natural language that could lead an analyser to incorrect conclusions Furthermore the text content is at most 140 characters meaning one word analysis misses could change the final sentiment category drastically Each of these challenges needed to be tackled by whatever approach was implemented The most basic approach attempted was use emoticon detection Emoticons emotion icons ex press feelings by replicating simple facial expressions using keyboard characters For example signifies happiness whilst D signifies severe dismay or anger The initial sentiment analysis approach checked each tweet for
77. ample application which aggregates daily data in this way is described in Section 4 9 3 4 Conclusion In this chapter a series of requirements was defined against which Pulse can be compared at the end of the project These requirements encapsulated required functionality as well as properties the final system should exhibit Each of the requirements can be broadly categorised as mainly involving user interaction or data processing From this the need for two distinct subsystems was established The first is a browser based user application for querying and manipulating stored Twitter data The second is a robust data gathering and processing application which can store tweets in an easily retrievable way Before a user application could be created a significant volume of tweet data was required The data processing application described in the next chapter is responsible for the entire data gathering process from initial download of each single tweet to daily indexing of millions of tweets 25 Chapter 4 Data Processing A significant volume of data was required in order to perform meaningful data analysis in the user application Developing a robust application to manage streaming parsing storing and indexing large volumes of tweets in real time was one of the key development challenges of Pulse This section describes the approach taken issues faced and the final application 4 1 Data Processing Requirements As the primar
78. and a set of optional query statistics not used by the final application The response is then returned to the client Slashtag queries are also handled by the TagQueryManager illustrated in Figure 6 6 Create new eS Set TOM Update TweetHashiag pasa options from TweetHashtag request Query Initialise TagQueryManager Figure 6 6 TagQueryManager Slashtag Process Overview return response The retrieval process for each tag of a slashtag query 1s identical to the process followed for a single hashtag However there are three main differences in managing slashtag queries e The query manager performs the entire lookup process twice once for each tag e The overall query process takes less time than if the two tags were queried for individually Slashtag queries are always for the same date so once Terrier has loaded that day s index the second tag query returns considerably faster e An InitialDuoResponse is used to return the results to the client This encapsulates an addi tional JSON string and statistics object one for each tag Temporal Range Queries Temporal range query requests described in Section 5 4 3 are sent to the TemporalQueryManager for processing The TemporalQueryManager handles interactions with the file system selection loading and parsing of the correct daily statistics files see Section 4 9 and construction of the correct response type This process is illustrated by Figur
79. ant engineering work undertaken during the project was the creation of a user application able to handle search queries and respond with appropriate data visualisations The following factors were considered whilst analysing candidate frameworks e Maturity the framework should be well documented and stable 19 e Ease of Use functionality in Pulse will be added at a rapid pace during the development process The selected framework should aid this iterative process with debugging tools useful libraries and a quick write test deploy cycle e Portability the final application should be runnable across multiple platforms and the ideal framework will innately support cross platform development e Framework Weight the framework should not require significant resources to operate and should be flexible enough to allow integration of non framework extensions if required e Deployment deploying the application should be a simple process requiring little additional software or hardware beyond the framework itself e Communication the user application needs some way to retrieve data for each query The framework used should provide a thorough robust communication mechanism for this pur pose Several frameworks and technologies were initially reviewed to understand which would best suit development of Pulse It quickly became clear that a web application rather than a traditional desktop application was the best way to proceed Browser base
80. any retweet is likely to reach a certain number of audience once the users tweet starts spreading via retweet 52 This process is visualised by plotting retweet occurences on a map 105 Whilst top retweet lists are commonplace on the internet for example The Retweetist News 3 no retweet mapping function was seen amongst existing systems Data Requirements This component uses the top retweets hashmap returned by the server in response to temporal range queries The retweet hashmap contains string keys representing the Unix timestamp for each day returned Each value contains a structured string with retweet texts as well as list of users user locations and publication dates for each retweet This additional information is used to map retweet propagation over time Data Preparation amp Visualisation This component features two views an ordered list of retweets by number of occurences and a map displaying location waypoints indicating how the retweet propagated geographically The initial view lists the top retweets by number of occurences for the query date selected A list box is also generated in the component title bar to switch between different dates Each retweet is contained within a box which when clicked shows additional statistics The gradient shown in each box is a visual indication of the retweet popularity with solid red representing the most popular retweets to solid blue representing the least po
81. ario use of meta index reverse keys was causing the UID lookup index merge process to run indefinitely Before discovering this was the case extensive attempts were made to try and complete the merger with the property enabled However each attempt required several hours before failure could be safely declared and a new approach tried 4 11 Performance Analysis The performance of the data processing application was measured using two metrics e Uptime how long was the application active during the run period e Data Integrity how many indices were created succesfully vs those that were corrupt 49 Test Case ID Directory for merging has 24 hourly indices Directory for merging has lt gt 24 hourly indices Clean up initiated on dailyIndices folder 1 supplied indices are corrupt Table 4 12 Test Cases Merging Test Input Expected Output Result FO IS A Indices directory with 24 1 daily index directory Success directory with sin ro o rr mler erated Indices directory with 24 1 daily index directory Partial as test case 1 if indices even number of index direc tories failure 1f odd number tory 1 corrupt indices Ignore index and continue Success corrupt indices with remainder ignored and remainder in dexed Corrupted tweet file Ignore file and continue in Success file ignored and dexing error logged Table 4 13 Tests Merging 3 Directory cleanup config Removal of all interim in Failure
82. art Daily Volume Chart Figure 5 27 Sentiment Volume Component Data Requirements This component uses the daily sentiment and daily volume string returned by the server in re sponse to temporal range queries The sentiment hashmap contains string keys representing the Unix timestamp for each day returned and a comma separated string value structured as follows lt category gt lt numTweets gt lt category gt lt numTIweets gt The volume string is a comma separated list of day volume pairs lt timestamp gt lt volume gt lt timestamp gt lt volume gt Data Preparation amp Visualisation Each of the views contains a single line graph mapping either sentiment or volume over time The views can be switched by clicking the View button in the component title bar The sentiment view features of a line graph with each category of sentiment from very positive to very negative plotted against each day The line graph is constructed using the Visualisation API The volume view also features a line graph plotting volume against time for the temporal range specified N B As discussed earlier Pulse stores only 5 of all public tweets As such the volumes displayed are not representative of total Twitter usage 104 Future Work This component was the last to be implemented and still needs further work to fully take advantage of the data available The aggregate sentiment values shown on t
83. ata analysis Whilst elementary manipulation can be conducted by the browser code intensive tasks are naturally suited to server execution For example components may choose to interact with a service when they need to e Utilise an external library or API that is unavailable within the browser e Perform large scale manipulation which would take unreasonably long in the browser e Access files on the web server In these cases the component only needs to construct a request and define a callback function to handle the response with the service handling the implementation detail in a language suited to the task 6 3 Suitability of Services Whilst services can be extremely useful in assisting components certain factors must be considered before opting to implement functionality on the server rather than client side e Data Transfer the amount of data being transferred between the client and server should be minimised as much as possible to maintain low network latency If a large amount of data needs to be transferred then the time taken to compute on the client side should be compared to the round trip service request time to see which is preferable e Task Type the task should be assessed in terms of how naturally it fits the client or server For example presentation based work naturally fits the capabilities of web browsers whereas intensive data handling will often be more suited to a seperate server application 111 e Effect on
84. ation Bar Options Panel discussed in Section 5 5 4 5 5 4 Navigation Bar The Navigation Bar shown in Figure 5 6 occupies the left panel of the user interface It is used to display query histories allow comparison queries and configure Pulse options To conserve space it uses a stack panel widget which displays only one panel at a time the others accessible by clicking on the relevant tabs Tag Query History Navigation Tabs Date Query History Compare lags Options Navigation Panel Hashtag Queries Retrieved Results 100 Figure 5 6 Navigation Bar Tag Query History Panel Figure 5 7 shows the Tag Query History Panel used to show a list of all tags queried for during the current Pulse session Each tag within the panel corresponds to a single query When clicked the results from the selected query are loaded from the query cache into the component area Furthermore hovering over a tag 62 Tag Query History Tag Query Figure 5 7 Tag Query History Panel displays an info window giving more specific information about the query such as for what day it was issued and what options were set Tags are added to this panel during the update workflow by the component manager However it is the Navigation Bar that constructs the relevant event handlers builds a popup pane to show on hover and positions the tag Date Query History Panel Figure 5 8 shows the Date Query History Panel used to display a
85. ations instead of the official Twitter applications How preva lent is updating via iPhone in Japan compared to updates via Blackberry These types of questions are not currently answered by the data shown in the component but could be with additional devel opment time The devices view also needs additional work to better handle large numbers of different devices The graph is difficult to read if more than 15 devices are shown and whilst options allow the end user to specify a limit they are happy with the component should be able to hide and show devices on demand 5 7 11 Sentiment Volume Component The Sentiment Volume Component shown in Figure 5 27 displays daily Twitter sentiment and tweet volume Purpose The Sentiment Volume Component has 2 functions The first 1s allows users to observe changes in sentiment across a temporal range Whilst topic based sentiment analysis gives a focused sentiment breakdown for a small result set aggregated sentiment volumes allow users to identify events which are SO pervasive that they have an overall effect on Twitter An early example of this was noticed on Valentine s Day where there was a noticeable increase in positively ranked tweets The second function of this component is an early attempt at judging total Twitter usage volume This view though functional requires further work 103 Switch to Vole Trend Switch to Sentiment Trend Daily Tweet Volume Device Sentiment Ch
86. ay w Trails 5 Tag Select List 26 Diagram Key 1 Change Y Axis Variable Allows the Y axis variable to be changed Available options are Countries Occurences and Unique Users 2 Change Scale Linear Logarithmic Change the scale type for the Y axis Can be either linear or logarithmic 3 Chart Type Tabs Change the type of graph displayed The 3 main chart types are discussed in detail later in this section Scatter Bar or Line 4 Colour Vartable Colouring can be used represent a third variable along with the X and Y axises Available options are Countries Unique Users Occurences Same Colour all points are same colour or Unique Colour all points have different colour The gradient below indicates what each colour represents 5 Tag Select List Choose specify tags to show on each chart Click Deselect All to reset this list 6 Change Scale Linear Logarithmic Change the scale type for the X axis Can be either linear or logarithmic 7 Change X Axis Variable Allows the X axis variable to be changed Available options are Countries Occurences and Unique Users Time and Order Alphabetical 8 Time Slider Scroll to watch graph points change over time Will only be visible if X axis is set to anything other than Time 9 Play Chart As Animation Animate the graph points over time Use the slider to
87. ble components must be developed in a uniform way to allow for future functionality additions Developing new components must be a technically simple process e Configurable a full suite of options should be available for users to specify exact parameters for system use and each query e Quick Development Cycle given the time available for development the system must be structured in such a way as to allow for quick prototyping and development of new function ality 5 2 Design Principles The Pulse client application was designed in adherence to the following principles e Application not Website the finished application should take advantage of the fact it is run ning in a browser but function like a fully fledged desktop application e Colour with Function though the application should be visually appealing colour should also be used for emphasis comparison and as a meaningful metaphor when displaying results e Show Don t Tell data returned from queries should at least initially be displayed in a visu ally compelling way rather than simply providing raw numerical data e Clarity not Brevity the comprehensiveness of results should not be overshadowed by poor application organisation or visual chaos Components should be distinct from each other stay within their outer boundaries and be clearly delineated All UI elements are styled using Cascading Style Sheets CSS CSS is typically used by websites for uniformly
88. calability would not be satisfactory should the application be deployed streaming a larger percentage of the Twitter public stream 4 6 4 Indexers Once the data files have been cleaned indexing can be begin Each index pipeline component is responsible for indexing one particular type of index as well as configuring relevant Terrier parameters For Pulse s purposes this means setting the name and length of the meta index key value pairs and disabling Terrier term pipeline options For example given the scope for unusual tokens appearing in tweets stemming and stopword removal are not utilised by the Pulse indexers Documents amp Collections Terrier represents document corpora using a Collection interface 34 essentially an iterator over a series of individual documents themselves represented by classes implementing the Document interface 35 Whilst it provides concrete implementations of these for popular document types Pulse required custom implementations in order to create its indices Each custom Document imple mentation is responsible for parsing and term extraction of a particular input type with the custom Collection class managing creation of and access to these documents The Collection implemented by Pulse TweetCollectionFromGZIP provides Terrier with documents created from the compressed tweet files produced during streaming The tweet source for each index type during hourly indexing is always the same however th
89. ch must be indexed and stored The platform chosen must be able to scale well to this volume of data in storage retrieval and indexing e Retrieval Functionality Data must be accessible in a way that is conducive to useful search that 1s a well defined query language should be available that provides the client application with all required data e Retrieval Efficiency Accessing data from the client application must be fast enough for real time search even with thousands of results The retrieval mechanism must also be resource light e Retrieval Effectiveness how balanced 1s the total resources required by the platform relative to the platform s performance e Required Infrastructure The chosen platform must be able to run on standard hardware available to the university e Platform Maturity The chosen platform must be stable for regular intensive use and have sufficient documentation for setup configuration and troubleshooting Two options were considered for storage and retrieval of data in Pulse e HBase an open source distributed versioned column oriented store built to hold very large amounts of data over clusters of machines using standard hardware 12 e Terrier a flexible efficient search engine developed at the University of Glasgow for index ing large scale document collections 56 To establish which was better suited to powering the Pulse data processing application each was compared against the abo
90. ched to the HTML document body Browser Window Application Dock Layout Areas East Zone North Zone 10 height 90 width Application Root Layout Panel Figure 5 2 Dock Layout Panel The North Area of the DockLayoutPanel is used by the Query Bar and contains structure panels that hold the query form together The West Area contains a StackLayoutPanel within which is the Navigation Bar The StackLayoutPanel is tabbed so as to hide any content not currently selected The East Area is managed entirely by the component manager used for displaying components For more specific details of how these areas are structured see Section 5 5 2 Above GWT is the Pulse Application Manager This layer combines simple widgets and content panels into more complex entities for placing in each of the GWT DockLayoutPanel areas In addi tion the application manager handles almost all user input from the Query Bar and the Navigation Bar Once requests are received by the Application Manager they are passed to the Component Manager This layer is responsible for executing all queries and displaying the relevant results It also manages the session query cache Components make up the final layer of the client application These are content units which ma nipulate and present data retrieved by the component manager to the user in a visually appealing way 56 5 4 Query Types Pulse features three types of query which can be issued by the use
91. cks oes a pa dd vath y SNEEN rr CIAL politicians z E 201 anor T Sa io ii gt He are T nS a i 7 thunder ie thunde thunder pb funeral Dernestocl1 ae Ganca Ga qa ra ernestocl joydia r colosio Marzo MES popeyes amistad tmitadunmitac m gt Gwaisluadakine Figure 2 3 TrendsMap TrendsMap is particularly good for allowing user exploration as every tag is connected to a stream of tweets which contain clickable usernames and tags However it is not possible to see how trends changed over time nor view any aggregations of trends from over a period of time As an instantaneous snapshot of Twitter trends Trendsmap provides some excellent features however in 15 terms of historical analysis or trend comparison it provides very little 2 2 3 Trendistic Trendistic 4 provides more traditional analysis tools charting tag usage over various time periods The site can be operated in two ways by typing a query tag in manually or by clicking on one of the suggested trending tags A graph is then generated visualising number of occurences over time Login with Twitter TRENDISTIC see trends in twitter show trends _ Powered by Index Tank technology Hot Chartl Japan s tragedy Japan and the tsunami by trendistic follow this user s Trending topics obama http ustre am e9fg http ustre am k07k fame live at twustream live at j weekend m Chelsea Hours displayed in GMT
92. contact centres ensuring customer satisfaction is high as well as reducing support costs 2 Providing a toolkit that would allow organisations to go more rapidly from a passive presence on Twitter to an active one would allow them to reap the same benefits Twitter is also a domain featuring unusual social relationships and ever changing trends Few tools exist that allow more than superficial analysis of the domain therefore creating an innovative system able to explore and visualise the Twittersphere in a way that highlights changes patterns and cycles through discovery is itself a motivating factor 1 3 Pulse System Aims The primary aim of Pulse is to allow for in depth analysis of data gathered from Twitter through an engaging user application From a system perspective Pulse should be capable of gathering a substantial volume of Twitter data for analysis This involves streaming data from the relevant data sources storing this data indexing it for later retrieval and performing batch analysis of the data From a user persepective Pulse should allow for extraction of meaningful insights from the data analysed It should present results in a user friendly and manageable way and allow users to navi gate results in both a directed and undirected fashion The information presented to the user should give insight into a particular topic or time that otherwise would be difficult or impossible to discover Pulse should also be extensible
93. covery Science vol ume 6332 of Lecture Notes in Computer Science pages 1 15 Springer Berlin Heidelberg 2010 10 1007 978 3 642 16184 1 46 W B Cavnar and J M Trenkle N gram based text categorization Ann Arbor MI 48113 4001 1994 47 Dai Clegg and Richard Barker Case Method Fast Track A Rad Approach Computer Aided System Engineering Addison Wesley Longman 1994 48 K A Ericsson and H A Simon Protocol analysis MIT press Cambridge MA 1993 49 Alec Go Richa Bhayani and Lei Huang Twitter Sentiment Classification using Distant Su pervision pages 1 6 2009 50 D J Higham P Grindrod and E Estrada Mathematics Faces up to Facebook 2010 51 ISO IEC 9241 14 1998 Ergonomic requirements for office work with visual display terminals VDT s Part 14 Menu dialogues ISO Geneva Switzerland 1998 52 Haewoon Kwak Changhyun Lee Hosung Park and Sue Moon What is twitter a social network or a news media In Proceedings of the 19th international conference on World wide web WWW 10 pages 591 600 New York NY USA 2010 ACM 53 J R Lewis IBM computer usability satisfaction questionnaires psychometric evaluation and instructions for use International journal of human computer interaction 7 1 57 78 1995 54 George A Miller Wordnet a lexical database for english Commun ACM 38 39 41 Novem ber 1995 55 Jakob Nielsen Usability Engineering Morgan Kaufmann 1993 148 56
94. ct as nodes The university does have a cluster available which can be used which already has HBase however the reliability of this infrastructure is unknown Platform Maturity HBase has yet to reach a complete 1 0 release and so is effectively still in beta development That said there are several active support outlets mailing lists forums etc staffed by users with length expe rience However as the potential platform for a central part of Pulse HBase is an unknown quantity Incompatibilities errors and other implementation concerns which apply to 1 0 type applications are all possible challenges The documentation for HBase however is fairly complete and comprehensive Table 3 1 HBase Suitability Analysis Terrier Terrier provides indexing and retrieval of large scale document collections in a highly customisable manner Table 3 2 shows the analysis of Terrier against each criterion 23 Analysis Scalability Terrier is highly scalable designed to index millions of documents in a time responsive manner It can also be used in conjunction with the Apache MapReduce framework to perform distributed indexing Retrieval Function Terrier provides a simple query language more than sufficient for ality the needs of Pulse which can be used to query any Terrier index Initial testing showed that even for large document sets response time was fast However testing with very large indices was not attempted Terrier Collect
95. ctivity The first walkthroughs introduces participants to the core functionality of Pulse through simple step by step activities Each clearly introduces a specific aspect of the system using exact instructions ensuring all participants are trained to the same level In addition the sample queries selected for each walkthrough were chosen so that they very clearly and explicitly demonstrated the purpose of the walkthrough The following functionalities were highlighted by the evaluation walkthroughs e Hashtag Search introducing the most basic type of query within Pulse e Hashtag Comparison By Date introducing the ability to compare the result of two different queries by date e Hashtag Comparison By Tag emphasising that two queries featuring different tags can also be compared e Options providing a brief overview of how options can be used to configure the query pro cess 125 e Slashtag Search introducing the slashtag query syntax for faster comparative searching e Time Querying introducting the concept of time queries without any query tag to see an overview of daily Twitter activity Whilst walkthroughs are necessary for user training they alone do not allow for task orientated usage To ensure participants had an opportunity to use Pulse with a goal in mind a second activity type scenarios was also included Scenarios require the user to find a particular piece of informa tion using experience gained from the
96. ctivity featuring the query tag It also allows users to investigate the individual text of tweets from a specific location Motivation Twitter provides several geographic meta data fields which can be used to map tweets Several tools exist already which provide this functionality such as TwitterVision 39 however as with many of the analysis tools evaluated in Section 2 2 most focus only on real time mapping of all public tweets Other web mashups exist combining Twitter data and Google Map functionality usually for a very specific purpose GeoChirp 17 is one such example a location aware utility showing only tweets posted by Twitter users geographically close by The Location Component implemented in Pulse primarily indicates popularity of tags by geo graphic location Concrete examples of the questions it can answer include what are people saying about google in China and how does this compare to users in the US or which coun try featured most chatter about the facup final No existing tool was found that plots tweets dynamically retrieved by topic where dynamically means in response to a search query 79 Map Marker Map Controls Figure 5 20 Location Component Data Requirements This component uses the location field from each tweet for geocoding and the tweet text displayed in a popup window after clicking a marker The query cache used by this component stores each marker 1 e latitude longitud
97. d applications remove the need for users to install any software provided direct access to a well defined presentation markup language Cascading Style Sheets or CSS 40 and naturally fit the data domain Twitter itself a pio neering web application Furthermore Twitter provides tweet data in JavaScript Object Notation format a data interchange type which browsers are extremely fast at parsing The data format and its implications are discussed fully in Section 4 4 jQuery The first technologies considered were JavaScript libraries such as jQuery 25 which simplifies client side HTML scripting in web applications as well as providing a series of rich interaction widgets jQuery is remarkably lightweight a 22Kb file which can be included using one line of HTML and has a large number of pre defined easily implemented complex interaction behaviours with are traditionally challenging to emulate in the browser It also supports AJAX Asynchronous JavaScript amp XML server requests with a series of useful abstractions masking cross browser idiosyncrasies jQuery is a mature platform used by 43 of the 10000 most frequently visited websites worldwide 26 It has extensive documentation and an active support community which would be invaluable during development should implementation issues arise jQuery is also easily deployed the library needs only to be uploaded to a web server Whilst jQuery greatly simplifies JavaScript development
98. de fans de Justin creen que el cumplea os de Jb es el 14 de Marzo RT si vos sabes cu ndo es hechosbeliebers RT Total 619 times across 8 different countries Map o Each retweet within the Retweet Paths Component is contained within a Retweet Entry The gradient inside the retweet entry is a color between solid red and solid blue denoting the popularity of that retweet visually Similarly the popularity of each tweet is also denoted by its position within the list the nearer the top the more popular the retweet The most popular retweets feature a solid red gradient and are at the top of the list Within the retweet entry is the original text of the retweet If this is in a language other than English a translation will be provided underneath In addition to RT text the volume of retweets and number of countries is also displayed The Map Button will attempt to recreate the path of the retweet from first appearance to last right Whilst this gives a rough approximation of the retweet path it is generated using a small subset of location data for the retweet Retweet List To return to the retweet list after vie
99. der only one particular aspect of the Twitter domain provide superficial statistics or do not give any indication of how information being used is gathered and processed The project consists of two major parts constructing an always on application which can stream and process the required data and constructing a user application which can display and manipulate the analysis results in a meaningful way 1 2 Motivation Creating a system such as Pulse is interesting for several reasons The sheer volume of data gener ated on a daily basis by Twitter is huge around 100 million tweets per day Developing a robust reliable and stable application which is able to handle this amount of data is an exciting technical challenge Meaningful Twitter analysis also has important practical applications in areas such as marketing sales and support As its popularity grows companies are becoming more prolific in their use of Twitter for customer engagement and brand management One example of this 1s Microsoft who have utilised Twitter as a tier 1 customer support mechanism Multiple staff operate the username XboxSupport 9 which provides proactive and reactive customer support to users having technical issues By monitoring mentions of their brand names responding to user complaints directly and tracking opinion within tweets mentioning their products they have managed to significantly reduce the number of support calls to their
100. derately Slightly Slightly Moderately Very t h ose tweets an d h ow succes sf u t h e Positive Positive Positive Negative Negative Negative Volume E 50 es 61 30 2 ana lys IS WaS Percent s 5 18 05 81 05 22 02 10 83 11 55 yl m 16 Tag Usage Volume Component The Tag Usage Volume Component charts the usage of the queried tag over the course of the query day divided into 24 hours gt 7 gt cad P P T e P a b r 4 ra a gt a A gt a Each of the bars can clicked for more information about that particular hour exact number of tweets etc Geographical Heatmap Component The Geographical Heatmap Component shows the usage of a particular term by country with the relative intensity of the colour indicating the level of usage US Tweets 109 Tweets BERR The scale in the bottom left indicates usage popularity However you can also hover over any country to see an exact number see top right 17 In Context Component The In Context Component displays additional ancillary information about the query hashtag User Data Tweeting Users Followers vs Following S a o a MM Users tweeting wikileaks En E 2 O LL S oa oa oa 4 10 000 20 000 30 000 40 000 50 000 Followers This component as with the Sentiment Analysis Component utilises a tab panel User Data
101. ds Pulse does not consider users ranking connections or usage patterns as part of data analysis This was a conscious decision based on the amount of time available to develop the software instead opting to focus on comprehensiveness of tag and temporal range queries In future however Pulse could easily be extended to incorporate user analysis Pulse already stores user information associated with every tweet 1t handles and the modular nature of components means that defining a new query type and creating user analysis components would be a task in data analysis not in application restructuring Specific component level improvements are detailed in Chapter 5 however in terms of cross component analysis there is much scope for improvement Components in Pulse are currently self contained units of functionality The next step would be to establish how best to share pro cessed data between components enabling much more granular analysis Developing a mechanism for components to disseminate then disclose the data they receive to fellow components would enable the creation of complex manipulable data views For example Pulse can currently both 144 detect sentiment of tweets and plot their location But what is the best way to implement a feature which shows sentiment only for a specific location Or highlights hourly popular links rather than daily links Pulse struggles to answer these more specific questions as it stands but with furth
102. e 6 7 Before the first temporal query is started by the manager 1t performs scans the Pulse data directory for daily statistics file and loads each file 1t finds into a cache These files are fairly small 30Kb and so can be held in memory for an entire application session This is achieved using a hashmap of Unix timestamp statistics object pairs On receiving a temporal range query request the manager needs only look in the cache keyset to see if the data exists If no matching key is found a no results response is immediately returned to TwitterQueryServiceImpl Each day s Twitter activity is encapsulated by objects of the class DailyStats with one object stored per statistics file The TemporalQueryManager is responsible for reading the necessary objects and converting the data within to a format suitable for transmission As we will see in Section 6 4 4 117 This intalises the manager loading sach daly stats file into memory Sel TOM options TemporalQueryManage Retum raana from request r processes query pe Creale new TemporalQueryManager Extract Extract Pencdic Extract country usage Return InitialPulse Return na results Extract top Extract top reatweet Sources data data sentiment volume data Figure 6 7 TemporalQueryManager Process Overview complex object serialisation results in very slow data transmission so simply returning DailyStats objects to the client was not
103. e data Check divergence from sync tweets received in Pulse appli current time If over 60 cation seconds discard tweet Table 4 6 Data Integrity Issues 44 4 9 Additional Batch Processing A separate data processing utility was developed to aggregate Twitter data on a daily basis The output is used to power temporal range queries within Pulse without needing to search tweet indices 2 files are generated by this application dailyStats log and dailyStatsTop10 log Each file contains a binary object of class DailyStats containing e Daily Hashtags each hashtag used throughout the day how many unique countries tag was used in number of unique users using tag and number of retweets tag featured in e Hourly Tweet Volume hourly volume of tweets across the day e Daily Sentiment summed daily Twitter sentiment See Section 5 7 8 for a detailed descrip tion of the sentiment analysis process e Retweets each retweet posted during the day the retweet count the publication time the location of the user posting the retweet and the usernames of each retweeter e Geographical Tweet Count list of countries tweeting plus volume for each country e Source Count list of unique devices used to tweet from plus volumes for each device e URLs Tweeted list of URLs posted on Twitter plus occurence count of each e User Mentions list of all Twitter users mentioned plus number of mentions e User Follower Following Counts list of all
104. e documents created for indexing vary Table 4 3 describes each of the three types of index created every hour The HashtagDocument class tweaks the standard FileDocument class to be more lenient in defining what a term is necessary given the form hashtags take e g they cannot be space separated may 39 Document Class Meta Data Hashtag HashtagDocument each document within the collec Tweet UID tion indexed is a comma separated string of hashtags for a given tweet Unique Identi DBRecordDocument each document within the collection Tweet UID fier Lookup In indexed is a unique tweet ID JSON entity dex Text Only Index TweetTextDocument each document within the collection Tweet UID indexed is the full text of a tweet This index is not currently used by Pulse Table 4 3 Index Types not be traditional words For each string of tags indexed the corresponding tweet UID is added as document meta data to the Terrier meta index The UID lookup index acts as a pseudo database for storage and retrieval of JSON tweets Each DBRecordDocument added to the index is a UID string for a given tweet The Terrier meta index then stores the full JSON entity corresponding to that UID This means the UID index can be queried for a particular UID after which the meta index can be searched to get the full JSON tweet for further processing There 1s a known issue at this stage involving the length of tweet entities Cursory tests of
105. e familiar with the interface paradigms of social networking sites and the web all of which Pulse is based upon Finally the time invested by users on social networking sites as a proportion of all time online is fairly high This level of user engagement with social networking itself will hopefully translate into a similar level of engagement with Pulse 7 4 Post Evaluation Results Analysis As discussed in Section 7 2 3 the post evaluation review was split into two discrete sections each of which is discussed below 7 44 1 Desirability Users were first asked to rate Pulse by selecting one or more words from a list of 106 adjectives Table 7 2 shows the results of this process Advanced 6 Reliable 3 Accessible 1 Inadequate 1 Bright 6 Responsive 3 Businessdike 1 Intimidating 1 Impressive 6 Appealing 2 Clean 1 Obscure 1 Usable 4 High quality 2 Counterintuitive 1 Trustworthy 1 Approachable 3 Innovative 2 Desirable 1 Unconventional 1 Attractive 3 Overwhelming 2 Easy touse 1 Understandable 1 C Buy 3 Powerful 2 Effortless Unpredictable 1 Energetic 1 Table 7 2 Word Selection Frequency Of all words selected nearly 86 were classified as positive which itself is validation of the application s user appeal Every participant described Pulse as useful whilst almost all described it as Impressive and advanced Satisfyingly from a usability
106. e is set This 1s available system wide providing components with the current query tag the query date and the options configured Then a new query cache entry is created for storing each component s data cache Existing components are cleared from the component area and new ones relevant to the current request type are generated Once all parties are prepared updating a request is made to the server via TweetFetch the class which handles all interactions with the index query service described in Section 6 4 Once finished the server sends a response back to the client and subsequently the component manager 65 Update Workflow QueryBar NewComponentManager TweetFetch Component Mon Cachod Construct request A E from user input Set Queres Oniy options query currentRequest date etc Create new query cache Update navigation bar Clear amp create components Se nent Display loading a ji Notify component manager Teale new service s Enact relevant Component update la mechanism updated i Reset eee currantResult A currentResponse Figure 5 11 Update Workflow 66 The component manager first checks whether any results have been returned and if so whether there are any errors in the results If so an error message is displayed to the user If not the re sults are parsed and each component is updated in turn using the appropriate update mechanism for that request
107. e pair geocoded as an array These can be instantly added to a map when retrieved form cache Geocoding As most tweets do not include explicit latitude longitude data the location field of each must be geocoded Helpfully the Maps API provides a Geocoder class which asynchronously attempts to geocode a given string However the API has both rate and frequency limits which if exceeded will cause geocodes to fail Typically in situations like these a scheduler or even Thread sleep could be used to stagger requests However with the final application compiled to JavaScript this was impossible A solution to this issue to use GWT s Timer class a mechanism for implementing delayed logic Timers can be set to run on a schedule a specific number of times with actions for perform specified in the Timer s run method The Location Component needs to throttle geocoding to roughly one request every 100ms As geocoding 1s asynchronous a callback was also defined to handle the response from each geocode The ExtendedLocationCallback stores the tweet text With the complexity of managing the plotting and geocoding process increasing a manager class ScheduledMapFetch was devised This class is responsible for holding the geocoding queue run ning the geocoding timer creating callbacks for each geocode and plotting the results 80 The final geocoding process is described in Table 5 5 Chass MapComponent MapComponent MapComponent MapC
108. e results which exclude Web as a device select this checkbox Trend Tag Whitelist Blacklist The tag whitelist and blacklists allows specification of which tags to only show or specifically which tags not to show from the day s top tags Any tag not on the whitelist will be ignored or similarly any tag on the blacklist will be ignored Clear List Clear Trend Tag Blacklist Clear To add new items click the button A text box will appear below type in either 1 tag or a comma separated list of tags then press Enter on the keyboard These tags will now be visible in the list Note Only one of the two lists may be used at any time Note for all options that use percentages the number returned may vary slightly from the percentage specified This is because a random sample of that percentage is taken However for anything other than a trivial number of results this will have little effect Time Query Results Once the time query has completed either the results or a no results found error will be displayed below Pulse No results for this date Close If there are any results the component area will now contain time query result components Time Query Components Successful time queries will result in five components being displayed inside the Component Area Trends Component Place Device ay P Component 5 Countries Same size 90 Dever Occurences
109. e specific detail Information displayed within each component should be unique to conserve screen space and eliminate redundancy Components are described in Section 5 7 The system must be easy to use by novices but also provide a highly customisable experience for advanced users The complexity associated with advanced use should be masked for those not requiring it yet easily available should the need arise Users must be able to compare the results of different queries to observe differences in tag use and time periods Components used for comparisons should provide additional functionality making clear the differences between each result set The analysis performed by the system must be accurate and aid users in identifiying trends within a single result set and across multiple result sets The data used to perform such analyses must be available for the user to review Querying and navigating the system to find particular pieces of information should be intuitive Given the large volume of data available for searching desirable data must not become obfuscated by poor structure or a cluttered user interace The application must encourage user exploration by providing organic paths to move from one query to the next Where appropriate information related to a query should be provided in addition to the direct query result set Finally all functionality should be clearly explained either explicitly using online help or implic itly thro
110. e vary from simple tools that highlight which of a user s followers are not reciprocating to more advanced tools that automatically follow users with low follower counts message users with promotional material and recommended users to follow based on their reciprocation rate e Brand marketing tools commercial tool packages are available which let businesses use Twitter to promote their brands analyse consumer opinions and track product mentions e Trend tracking tools applications which allow users to track particular topics over time displaying how usage changes and highlighting times where usage deviated from what would be typically expected This project is not focused on user tracker or aiding user self promotion so analysis of sample applications in these categories has been omitted Of the tools categorised four were chosen for deeper analysis to understand Twitter analytical ca pabilities generally available and also to identify features not currently common amongst tools 2 2 1 Twitter Search Twitter Search is shown in Figure 2 2 Twitter Search can be used to query what s happening on Twitter in real time with the results page automatically updated as new tweets are posted Twitter Search is an interesting tool for casually exploring popular topics at a point in time It does however have its limitations Queries can only be issued for roughly the last month of Twitter activity all older data is archived and inacce
111. ed within the query cache a store of all data retrieved and created throughout a Pulse session The query cache allows users to quickly retrieve and display results generated previously rather than re querying for the same term 68 Each query is stored as a QueryCache a collection of all the data required to generate components for a query without making a server request In order to use this data each component then specifies a means through its updateFromCache method through which this data can used to re create the component During a query s initial update phase each component supplies the cache with a cache object specific to that component Usually this will not contain the raw results of a query rather the results after manipulation within the component Though this is more storage intensive requiring each com ponent to define its own cache requirements it significantly reduces time to generate components from the cache particularly where the component performs significant data transformation on the raw results of the query or makes a server request Individual component cache specifications are described in Section 5 7 Access to the query cache is managed by the QueryCacheManager providing safe access to the session cache Components need not be aware of the wider state of the system only that they must store their results in the cache supplied by the QueryCacheManager In exceptional situations where a cache other than cur
112. ee using all returned ema queries much tweet UIDs i faster query iis result set m ocids Create new TerrierSearch with correct UID hookup index Figure 6 5 TagQueryManager UID Lookup Stage 2 shown in Figure 6 5 uses the retrieved UIDs to lookup each tweet from the UID lookup index The UID lookup index stores every tweet for a single day indexed by UID This means that by supplying a query string containing space separated 33 UIDs Terrier will return the complete set of tweet document IDs that those UIDs represent The document IDs are then used to extract the actual JSON tweet strings from the meta index which are returned to the TagQueryManager as an array The pseudocode for this process is shown below resultSet Terrier query process results metalndex index getMetalndex tweets new String array for ali document mas returned tweet metaIndex getItem jsonTweet documentID tweets add tweet return tweets This query based approach though not the fastest does return all tweets required to fulfil the client request Terrier also provides reverse key lookups where document IDs can be obtained by defin ing a meta index key which is unique for every document To enable this the index must be created with 1 or more reverse keys specified via Terrier s indexer meta reverse keys property and the index merging process must include the appropriate index structures in each merge enabled via the merger meta
113. elmpl which accepts a SentimentAnalysis ServiceRequest containing the tweets to be analysed and the sentiment analysis options These are analysed as described in Section 5 7 8 and the results are returned to the client callback func tion as a SentimentAnalysisServiceResponse The response contains the total number of tweets per category and a sample of tweets from each if any Once analysis is complete the category totals are used to create a pie chart of overall tweet sen timent as well as an explicit data table Both of these elements are displayed within a TabPanel along with the sample tweet category table Options The Sentiment Analysis Component has two options Use POS Tagger and Analysis Selecting Use POS Tagger will enable the POS tagger during sentiment analysis rather than just checking all occurences of each word in the dictionary For large results sets this may slow down the application The Analysis option allows users to specify what percentage of the result set should be analysed This can be reduced for queries with very large result sets Math random is used to ensure the sampled results are selected randomly from all available results Component Testing Table 5 18 shows the test cases defined for the Sentiment Analysis Component There are some specific edge cases applicable to this component Even if the general result set returned to Pulse has one or more results the component options may result in 0 resul
114. en using abbreviations slang or multiple words without spaces This was the case in almost all of the mismatch scenarios As 14 is still a large mismatch percentage JCTL categorisation was not implemented in the final system The Google Translate API was also tested to see how effective it was at detecting language How ever initial benchmarks showed the round trip time for each request was between 50 and 200ms far too long for real time usage with a non trivial number of results As it stands foreign language tweets are still analysed and counted towards the overall total Fur ther work is required to find a more effective method for eliminating non English tweets without removing English text in the process A first step towards this would be an option to pick one of the three options presented however due to time constraints this was not implemented 5 7 9 Trends Component The Trends Component contains a multifunction pivot chart which allows for analysis of daily trend data in a number of formats comparing multiple variables An annotated version of the component is shown in Figure 5 25 Diagram Key The following key applies to Figure 5 25 l Change Y Axis Variable Allows the Y axis variable to be changed Available options are Countries Occurrences and Unique Users 97 Colour 3 Chart Type Tabs Countries 60 13 Chart Area 1 Change Y Axis Variable 2 Change Scale Linear Logarithmic
115. ent and shared interests Figure 7 3 1 illustrates the reasons participants selected for using social networking sites 129 PreQ SN Q2 Usage Reasons 7 6 5 4 3 2 1 0 Figure 7 2 Social Networking Usage Reasons The most common reason for social network usage was for keeping in touch with friends and family Those participants that also selected sharing interests all selected stay informed follow figures and discussing as well This suggests that those using social networks only to stay in touch used Facebook alone and participants selecting other options had presences elsewhere on the web to satisfy those requirements Figure 7 3 1 illustrates the amount of time participants spent using social network sites PreQ SN Q3 Time Spent 1 All time i 7 No time Figure 7 3 Social Networking Usage Level Social networking usage was very common amongst participants with the majority spending be tween half and all of their time online using them For those participants who use Twitter this means understanding the functionality Pulse provides in Twitter terms hashtag trends top retweets etc should be considerably easier For those uninitiated to Twitter it may prove challenging 130 7 3 2 Trend Analysis Experience The expectation of the majority of participants was that they would not have any prior trend analysis experience Figure 7 3 2 illustrates those that had and had not PreQ T
116. er development time cross component integration could remedy these constraints Evaluation of the finished system also identified areas for improvement that would enhance the user experience Pulse currently lacks any significant online help and with so many components the results can initially be overwhelming This initial mental workload could be alleviated by creating an animated walkthrough stepping users through their first query explaining the application at a high level then highlighting components and explaining their function More general future work needs to be done to improve application responsiveness with large result sets Caching has been implemented wherever possible but queries returning several thousand re sults taken unacceptably long to complete The approach here would be twofold enabling reverse key lookup whilst indexing and resolving outstanding issues this is known to cause in the merg ing process Without the intermediate UID lookup query stage application responsiveness would increase significantly 145 Bibliography 1 TechCrunch hetps Lecherunch com 2006 07 157 is twttr interesting July 2006 2 Social Media Examiner How Microsoft Uses Twitter To Re duce Support Costs http www socialmediaexaminer com how microsoft xbox uses twitter to reduce support costs July 2010 3 The Retweetist News http retweetist com December 2010 4 Trendistic http trendistic com December 2010 5
117. er components display Component cache is cre ated but empty Output from one of test cases 2 Slower retrieval of results output from test case 1 2 Output from one of test cases 1 2 3 or 4 Quick display of output from one of test cases 1 5 A message 1s displayed in side the component all other components display Result Success component dis plays each tab with correct data Success a No results found error message dis played inside the compo nent Success component dis plays relevant tags or error message depending on analysed Success POS tagger op tion read by component and passed to servlet Success component dis plays as if query were a hashtag query Success data loaded from cache and displayed within component Success played message dis Table 5 19 Tests Sentiment Analysis Component 95 Duo Equivalent The Duo version of this component provides analysis of both result sets displaying the results within 2 tab panels These can be switched using a drop down list in the title bar of the component There are additional test cases to be considered in the Duo component these are listed in Table 5 20 Consider a slashtag search where one result set is large and the other comparatively small If the sentiment analysis is set to a low figure then it may be that only the larger set will actually have any sentiment analysis results In this case the component must
118. ered in the pre evaluation questionnaire e Social Networking Experience the most general category to understand participant s con fidence with basic social networking their usage habits and preferred means of networking interaction Understanding the casual user response will help determine the accessibility of Pulse e Twitter Experience Pulse requires a basic understanding of Twitter to fully utilise the fea tures it offers Understanding how much knowledge of the Twitter domain each participant has allows for comparison of results between novice and expert users and whether prior experience affects usability or desirability e Trend Analysis Experience the most specific category aimed at those with experience in tools similar to Pulse Understanding the opinions of experienced participants is critical for assessment of Pulse in the wider context of other social media analytical tools 7 2 2 Evaluation Tasks Creating evaluation tasks for Pulse was challenging due to the exploratory nature of the system Pulse encourages exploration by providing natural information discovery paths between each query as such there is no ground truth against which the system can be compared to objectively rank its success However participants must still be guided so that each has a uniform baseline experience to rank once the evaluation is complete To overcome these difficulties the evaluation task list incorporates three types of a
119. ers on Twitter can share information outwith the 140 characters limit and offer the potential to connect Twitter trends to the wider social web In con junction with retweeting Twitter has provided an easy way for users to rapidly propagate content Many sites exist which are entirely devoted to Twitter link tracking One of the most popular TweetMeme 37 specialises in comprehensively reporting popular links by category date and other filters The Popular Links Component was developed late in the project timeline and so requires further work to match existing tools Suggested ways of doing so are explained in the Future Work section below As 1t stands the Popular Links Component gives a very high level overview of link usage on Twitter ordered by number of occurences 108 Data Requirements This component uses the top links hashmap returned by the server in response to temporal range queries The links hashmap contains string keys representing the Unix timestamp for each day returned and a comma separated string value containing each link and the number of times it occurs shown below lt link gt lt numOccurences gt lt link gt lt numOccurences gt Data Preparation amp Visualisation This construction of this component is identical to the Retweet Paths Component explained in Section 5 7 12 Options The number of links shown can be set from the Options Panel in the Navigation Bar The default value is 20
120. ers onto Java web servers 15 such as Apache Tomcat 14 There were however certain drawbacks to using GWT The framework itself is still in its infancy This can be seen both in the level of documentation provided for certain features often none or at best a sparsely populated Javadoc description and the relatively low level of developer support compared to lightweight options such as jQuery Furthermore GWT has a very rigid structure that strictly defines the application Though perhaps useful early in the project whilst developing prototypes there is a risk that tight structure could become a hindrance once more experienced with the framework 21 3 2 2 Framework Selection Ultimately GWT was selected for use as the user application framework Being able to write web application code in Java and all of the development advantages this entails meant the prototype could be turned around very quickly A solid communication structure is included and produc tion deployment is a trivial process Full details of user application development are provided in Chapter 5 3 2 3 Storage amp Retrieval Having decided on a framework to structure the user application it was necessary to consider how data would be streamed and stored from Twitter This primarily involved reviewing available re trieval systems and comparing them against the following attributes e Scalability Pulse requires millions of tweets each day to function each of whi
121. ery date Instead an LRUMap could be used to store multiple TerrierSearch instances for various dates The benefits of using preloaded indices are obvious compare preloaded scenario 8 with unloaded scenario 7 for example and would over an application session greatly improve query responsiveness Temporal Range Queries Timing values were taken for each of the following operations using a sample of 36 daily statistic files 121 1 Chent Server transmission 2 Response creation 3 Server client transmission Given that all files are cached on initialisation and that the processing performed is mainly string manipulation it was expected that temporal range queries would be very quickly processed and returned to the client Table 6 3 shows the different scenarios used for temporal query performance testing Additional Days 1 2 1 either side 20 10 either side Table 6 3 Temporal Query Scenarios Table 6 4 shows timings taken for each operation all times are in milliseconds Cli Serv Trans Serv Cli Trans 2 3 71 28 2l 2 112 3 328 Table 6 4 Temporal Query Performance As expected each query returned in tenths of a second With several hundred daily statistics files this may have taken longer however the non linear increase in retrieval time suggests that the process has a baseline fixed cost on top of which additional statistics files add very little processing time This is also well withi
122. ese are used by the participants Those with accounts primarily used them to follow celebrities media personalities or news Interestingly only 1 participant actually posted tweets consumption of content being more prolific than production in the Twitter domain for this user group PreQ T Q3 Platform PreQ T Q5 Update Frequency Figure 7 7 Twitter Access Method Twitter Usage Frequency Figure 7 7 a shows how participants accessed Twitter whereas Figure 7 7 b shows the frequency of use Unsurprisingly Twitter usage from the desktop in a browser is the most common access method across participants this is also true for every day that Pulse has data for In terms of update frequency participants are split between high activity and very infrequent users with little middleground potentially signifying that users need to catch the Twitter bug before it becomes a regular part of their social interaction online The apparent lack of interest by some participants in Twitter may also have an effect on user en gagement with Pulse if the source itself is not of interest to a user will visualisations and analysis of data from that source be any more interesting 132 PreQ T Q9 Retweet Posts PreQ T Q10 Retweet Reasons Most posts Many Some Few retweets retweets retweets retweets Figure 7 8 Retweet Frequency Retweet Situation Figures 7 8 a and 7 8 b focus on the participants retweeting frequency and under what si
123. ests News organisations O L L O Recommendations from O others By following public timeline LI 7 Do you actively promote your own Twitter account Yes C No C 8 Ifso how particularly if you use any tools to do so i SOS 9 How often do you retweet others posts Most posts are Many posts are Some posts are Few posts are retweets retweets retweets retweets O L L O a Never 10 What are the main reasons you would retweet another user s post Useful to your own News you feel ts followers LI important a 11 What other web services do you connect your Twitter account to if any Facebook Social Networking site LI aag LI 12 When posting updates is the content primarily In relation to news or Work related C other s posts C 13 Compared to other web services social networks blogs commenting etc how much of your time is spent using Twitter 1 Most time 7 No time on on Twitter 2 j z i Twitter a O L L a L E Appendix C Post Evaluation Review 192 ci E sess cs Post Evaluation Review C ao Morte O Addition of word list 0 1 Initial draft Information Overview Now that you have completed the user evaluation tasks we would be grateful if you could complete the following tasks related to your experience using Pulse This consists of 2 sections e Word Selection amp Discussion We d like you to pick as many words as you like from the list
124. esults A cached hashtag query A cached hashtag query UN with no map markers 7 A click on a map marker Map with markers as geocoded for each tweet Map with no markers Output from one of test cases 2 4 Output from one of test cases 3 Output from one of tests cases 1 3 Info window displayed above marker with tweet text any existing open info windows closed Existing geocoding is can celled and output is from one of test cases 1 4 A new query issued whilst geocoding existing query Table 5 7 Tests Location Component 82 Output from test case 2 Success map shown with markers for each tweet Success map shown with no markers Success map is shown with appropriate markers Success component dis plays as if it were a hashtag query Success map is shown with cached markers Success map is shown with no markers Success an info window is generated with tweet text existing windows are closed Success geocoding is can celled and restarted with new data Duo Equivalent The Duo version of this component shows markers for both queries in different colours Addition ally markers can be toggled on or off by clicking icons in the component title bar This additional functionality resulted in two new test cases shown in Table 5 8 Test Case ID Both maps have geocoded results to show Only one map has geocoded results to show Neither map has geocoded results to show Table 5 8 Tes
125. etails Amendments per CM recommendations initial 0 1 1 overview in Quick Start branched Installation to Administrator s Guide 0 1 Contents so seekers N ceo EAEE EREA ete eee eens EE eee 4 MATINEE TS TWICE suscritos 4 TCS SA reci 5 Use the Search BOX EM Chey yiii aeaieie aiia ainiai 7 Dont Searen PNO aieiaeo A E ANE E E E A 7 Use Options to ING OSS CI pes eczcs ace xtc ce tn canever ares sees so eatanetessaestonserseeosessen EES ASEE EREE 7 e o e 8 ei A E E E E E 8 OUE ir 9 Nav gaton Bai reena Ee R E EE E A 9 Tag Query Navigation Panel csessssssssssssssssssssssssssssssessssssssesesssssssssscsssssessessssseseescsssesessssssssesseseess 10 Date Quen FARON Panel aea re rere entre nearer mere o ico rere errr 10 Compare ads Panel carcasa 10 TO INS Panas 11 Component A OA 11 QUIN UI TOPS a 12 Hasta do USA Picasa earn ponen 12 Hashtag QUery OPUS aciitajiiaia id 12 PVN EAC QUE SSNS sar arn ion soio casona asus eses 13 Hasta QUE COMPONEN Siesta ratas 13 Tagged With COMPONEN E eccciocoscinicosicineocd iii 14 Location Componerse iir a EEEE E TE EERIE E EI EERE ce 15 Sentiment Analysis COMPONEN cccsessssssssessssesssssessesssssessesesssessesesssesessesssssesseseseseseesessseseesessessessess 15 Tag Usage Voume COMEN eana EE T E A TE 17 Geographical Heatmap COMPDONEML escri naa 17 in Context COMPONEN Tissisadisns ciel nicieddiioicieli aoaaa raona ostaen aannaaien 18 Sta US iaa 18 Slashtag Query OPIO Sssnsnaapnanancaa ranita 1
126. etrieval It should be an always on application down loading data without a human operator being required The application should also be robust and fail gracefully on error maintaining state and being able to pick up from where it failed The full development process of this application is documented in Chapter 4 The Application Data Store is the central repository for storing all of Pulse s data files Whilst all Pulse components may freely access this folder the user application must utilise a server side ser vice to retrieve results the Terrier indices The structure of this directory as well as an explanation of its contents is also provided in Chapter 4 The User Application is the front end in which users can make queries view results and manipulate 24 User Application Server side Services Client Application Figure 3 1 System Architecture data operating on a two tier client server model Requests for data made by the user are passed from the client application to a server side service which processes the request retrieves the necessary data from the Application Data Store and returns it to the client A detailed description of the client application from inception to testing 1s given in Chapter 5 A detailed description of each server side service 1s given in Chapter 6 Other applications may be needed to amend update or cleanse the streamed data each requiring periodic access to the data store An ex
127. ext javascript src http maps google com maps file api v 2 amp sensor false amp key ABQIAAAAUF5VcB7UH od3DosLmwT BQrOhho5J3TAk93 AaOLIOHABI7TBRxYEuUrp3FSZh4Rq93_ bhTluilSQ gt lt script gt To this lt script type text javascript src http maps google com maps file api sv 286sensor falseskey YOUR KEY HERE gt lt script gt Compatibility This is the first Pulse release and so it has only been fully tested using Google Chrome As such we recommend this browser is used when working with Pulse It can be downloaded at attp www google com chrome Windows Mac Linux compatible Attribution Portions of this document are modifications based on work created and shared by Google and used according to terms described in the Creative Commons 3 0 Attribution License 208
128. f the US air force blocks staff from websites carrying FWikiLeak cables World news The Guardian http ff im vfPr1 map as well as the mouse scroll wheel or by dragging the map Each marker can also be clicked revealing the content of the tweet left Click the cross to close the tweet text information window Sentiment Analysis Component The Sentiment Analysis Component processes the text content of each tweet and tries to establish the sentiment of that text This component displays inside a tab panel Clicking any of the tabs along the top of the panel will change the content In this component you can view a pie chart summarising overall sentiment review some samples and their individual sentiment or view numerical details of how the chart was created 15 Gcovua ae 00 00 eses N O00000 006 O0 00 6eeses 00 060 00 00 00 se E 000000 00 00 00 PESTE 0000o 86 00 00 suso se 00 0000 oe C os 000000 490000 100000 e 0000 00000 00000 Tweet Sentiment towards wikileaks Component Tabs W Very Positive Ml Moderately Positive B Slightly Positive MA Slightly Negative M Moderately Negative WB Very Negative Tab Panel Area Chart Tab The Chart Tab shows a pie chart of overall sentiment divided into 6 categories very positive moderately positive slightly positive slightly negative moderately negative and very negative Each tweet is assigned a score and it is this score that determines the level of se
129. for example However as sessing the desirability of a system through standard quantitative means is subject to various user biases The post evaluation review is divided into two distinct sections one aiming to assess desirability and one assessing usability Desirability Analysis A method was developed based on tools created by Microsoft to establish how desirable participants found the system The process as described by Benedek et al 44 follows 1 Participants are given a selection of reaction cards each containing a single adjective 2 Participants must then sort these cards and select which apply to the product being evaluated 3 The cards are then filtered further with the 5 most relevant cards remaining 4 The evaluator then conducts a guided interview with participants to understand the reasoning behind selecting each of the 5 remaining cards This process had the potential to take too long given the 45 minute limit of the evaluation so instead of reaction cards a word list was created Participants were asked to initially check each word which they felt applied to Pulse then circle the 5 most relevant A short guided interview was then conducted to understand why those particular words had been highlighted Whilst this process is undoubtedly more time consuming than a questionnaire it provides a wealth of qualitative data which can be used to directly improve the user experience It also helps to offset the inevit
130. formation and plotting markers on a map illustrated by Figure 5 29 Figure 5 29 Retweet Paths Component Map The green map marker signifies the first occurrence with blue overlay lines showing connections between each subsequent retweet The last retweet is indicated using a red marker Each retweet path mapping is limited by the fact that Pulse only collects 5 of public tweet data The tweet will almost certainly have been retweeted in many more locations the exact number being included as an attribute in the JSON tweet entity However it serves its purpose well as a rough illustration of waypoints throughout the retweet s propagation path Options The number of retweets shown can be set from the Options Panel in the Navigation Bar The default value is 20 Future Work The main focus for improvement with this component concerns the propagation mapping function ality Ideally a user would click a balloon and see context information surrounding that particular 107 retweet the user the time of day and so on With this information it would be possible to identify users responsible for particular retweets 5 7 13 Popular Links Component The Popular Links Component shown in Figure 5 30 highlights URLs most featured in tweets on a specific day Link Link Entry Occurences http bit ly shorty 483 occurences Figure 5 30 Popular Links Component Motivation Links are the main method through which us
131. g Based Queries The TagQueryManager handles interactions with the file system selection loading and querying of the correct Terrier indices result parsing and construction of the correct response type This process 1s illustrated by Figure 6 3 Sal TagQueryManager options from request TagQueryManager Construct and processes query retum response Create new TweetHashtagQuery This reads in the available directones in fhe data folder and stores which days can be guened a G Run query for Checkifdata Ha h o l JSON entities for day exists ata ui Y i ia in UID lookup ania index Parse results into JSON string Return JSON Figure 6 3 TagQueryManager Process Overview Earlier versions of Pulse used a series of filters to reduce the result set size before returning to the client This was deprecated once HBase had been dropped as the main data store however the class TweetHashtagQuery remains in use It simply encapsulates the date and tag of the current query and is passed to the TagQueryManager by the service entry point The manager first checks the data directory to see what days are available for querying Should the manager receive a request for a day with no data it will immediately construct a no results response and return this to TwitterQueryServiceImpl Otherwise it will attempt to search for the tag supplied Tweet lookup is conducted in two stages 1 The relevant
132. g Terrier on a hourly and daily basis within the data processing application Indexing within the data processing application is triggered on reaching specific timing values Each time a tweet is parsed the publication date 1s inspected and compared to a hourly flag The hourly flag is initially set on application startup to one hour after tweet streaming has begun When the publication date of a tweet is equal to or after the hourly flag the hourly indexing process starts and the hourly flag is pushed forward one hour Should the application have started at some arbitrary point during the day rather than having been running for multiple days the hourly flag will automatically reset at midnight Before full indexing begins the appropriate folder structure for indices to be placed in is created This structure is described in detail in Section 4 2 2 Additionally the individual index pipeline components are created and added to the index manager It is at this point that each indexing pipeline component has its tweet source index destination and desired index type configured 4 6 2 Indexing Manager The indexing manager is responsible for maintaining order in the pipeline through which tweets pass before during and after indexing Every component in the pipeline runs within its own thread separate from the main streaming thread including the indexing manager itself Running the three application threads concurrently was necessary to ensure s
133. g passed to it has been checked for integrity and queries which return no results are caught before components are updated Structurally components need only be created with a relative width and height This is then made absolute by the component manager within the bounds of the component area Should application level changes be made to Pulse s structure these are instantly propagated to each component Given the number of components and types of query it was also essential to have some entity which could oversee and operate an end to end update workflow from user request to component presentation This process involves communication between many different parts of the application so it was necessary to have an intermediary which could orchestrate and implement each stage in the process Finally cache storage and retrieval could become disorganised if each component had to individ ually determine whether it was required and if so what cache was to be used With a component manager Pulse can store retrieve and propagate the appropriate cached results to the right compo nents with components only needing to know how they individually deal with those results 5 6 2 Update Workflow Figure 5 11 illustrates the workflow followed for each query from request to completion The workflow is initiated by a request being formed by the Query Bar This is then passed to the component manager On receiving this request the currentRequest variabl
134. ge by country The two views can be switched by clicking the View button in the component title bar The devices view consists of a line graph with each device in the top devices hashmap plotted against the number of times it was used to post a tweet This is done for every day in the temporal range showing variance in device usage over time The line graph is drawn using the Visualisation API The maps view shows a version of the Geographical Heatmap Component but instead of plotting tag usage by country this map shows overall daily tweet usage The map can be changed to show a different day by picking a date from the title bar date list 102 Options Web is by far the most popular method chosen by users when they update Twitter By selecting Exclude web from devices in the Options Panel of the Navigation Bar Wen will not feature in temporal range query results This makes differentiating between devices in the Device graph much easier The number of devices shown can also be adjusted The default value is set to 10 and though this can be increased it quickly clutters the devices graph Developing a more robust version of this view is listed as a future development Future Work Whilst the visualisations provided by this component are useful as they are there is scope for in tegrating the two data sets so that device usage per country is also analysed For example which countries tend to use unofficial applic
135. he results should load considerably faster This is because certain components are using less intensive methods to get results faster as well as caching of results by the server 7 To compare the differences in results click the Compare tab in the Navigation Bar select fb and fb in each of the drop down menus and click Compare You should see slightly different results in the Location and Sentiment Analysis components For full details of these Options see the Options chapter of the user guide Walkthrough 5 Slashtag Search In the Query Box search for apple google selecting data February 21 Once the results have appeared note which tag had more uses in the U K by selecting the Comparison option from the Geographical Heatmap Component drop down list and hovering over the U K 3 Slashtag searches can be used to quickly obtain comparisons of 2 queries on a particular day These results are also cached individually so that each can be individually compared against any other query However slashtag searches do requires 2 separate index lookups so once retrieved use the tag query history to retrieve this if required again Walkthrough 6 Time Search 1 Clear the Query Box if there is any text in it 2 Without typing anything in the search box select date February 26 and click Go 3 With no tag specified Pulse will retrieve daily trends for the day specified if it has the
136. he sentiment line graph do allow for observation of unexpected changes over time The volume view should explain the caveats associated with only storing a subset of the data and also give estimates as to total tweet usage These are small changes that would significantly increase the usefulness of this component 5 7 12 Retweet Paths Component The Retweet Paths Component shown in Figure 5 28 focuses on analysing retweets in terms of volume and propagation Ep RI ladygonga Dizem que a Lady Gaga copia a Madonna em Express Yourself Na verdade a Restart copia os Telettubies e o Justin Bieber c RT hechosbeliebers El 5 de fans de Justin creen que el cumplea os de Jb es el 14 de Marzo RT si vos sabes cu ndo es hechosbeliebers RT Total 619 times across 8 different countries Map Retweet Translation Retweet Entry RT TEAMFOLLOWBACK WANT MORE FOLLOWERS ReTWeeT for 50 new followers now LeamFollowBack T Figure 5 28 Retweet Paths Component Motivation Retweeting is particularly interesting facet of Twitter When a users retweets a tweet they are showing a level of agreement with the tweet content and an inclination to share that content with their own follower group The mapping element of this component was motivated by an study conducted by Kwak et al of whether Twitter is a social network or news source They found that no matter how poorly connected a user is
137. he tag spike in occurences every day or week or month identify changes in tag usage and understand tag usage context i e is the tag localised to one country or used worldwide Is the tag repeatedly used by a small number of users or widely used 99 Motivation The Trends Component enables users to identify particular tags which were trending on Twitter during a period of time A Twitter trend is a word or phrase which is being aggressively used by a large volume of users usually in response to some event e g superbowl but also occasionally through concerted effort on the part of Twitter users e g justsaying Trendistic discussed in Section 2 2 provides a basic implementation of the Trends Component graphing tag usage over time It does not provide any comparison functionality nor does it feature variables other than the number of tag occurences Whilst number of occurences is certainly an important metric for judging tag popularity there are other factors that need to be considered Is the tag trending in a single region or worldwide Is the tag being spammed by a small group of users or has it grown popular organically used by a large group of unique users Unlike Trendistic the Trends Component also provides tag comparison Each daily top tag can be manipulated independently of other tags or in combination with any number of tags When comparing several tags the Trends Component will aut
138. he tasks please consider the following e Clarity in the wording of options menus dialogs and errors e Ease of navigation around the system e Ease in finding particular pieces of information e Usefulness of each component e Speed and performance e Overall look and feel During the Evaluation You will have access to the Pulse user guide throughout the evaluation Please make use of this as you conduct each task Certain tasks will ask you to note information Once you have found the required information please note it in the notes sheet Leaving the evaluation You are free to pause or leave the evaluation at any point If you would like to pause or end the evaluation please speak with the demonstrator Questionnaire Once you have completed the task list please speak to the demonstrator They will then provide you with a short questionnaire to garner information on your experience using Pulse It would be greatly appreciated if you could answer as fully and accurately as possible bearing in mind the above points Tasks Walkthroughs The first tasks of the evaluation are designed to accustomise you to the system providing an overview of each key feature Walkthrough 1 Hashtag Search 1 Search for gaddafi selecting date March 1 2 Once the results have appeared note the number of times gaddafi was used as a hashtag on this day Walkthrough 2 Hashtag Comparison Search by Date Search for gaddafi
139. here is a failure that requires attention 50 Event Type 24 1 2011 Software Java heap memory set too 28 hours Restarted application with Failure low application failed Java Xmx option in creased 1 2 2011 Software Unable to reconnect after 5 hours Fixed reconnect bug 25 2 2011 Hardware Power disconnected to 48 hours Restarted application on Table 4 14 Robustness Analysis Furthermore the machine running the application was not protected with an uninterruptible power supply In a real world scenario the application would be running on a machine or cluster which could guarantee certain physical properties failover to UPS on power failure or seamless transition to another server on hardware failure using virtualisation The application recovered well from each error Where possible it restarted from a previous run ensuring to check for existing data folders If a day had been missed it created new directories and simply restarted streaming In terms of valid indices the application performed extremely well It is testament to the stability of Terrier that every index created during the run period was valid with no errors bar memory require ments occurring during the whole period Furthermore each set of hourly indices were successfully merged into the appropriate daily index for every full day of stable operation 4 12 Conclusion The data processing application is a complex timed workflow that was designed to be rob
140. hing for faster retrieval In terms of the wider application architecture services as implemented by GWT are ultimately Java servlets A servlet is simply a Java class which extends the capability of a server by means of a request response programming model 41 Servlets are typically extended from the standard Java HTTP servlet class HttpServlet 23 how ever GWT includes an extended version of the standard class RemoteServiceServlet class 31 110 which handles client server data serialisation and ensures invocation of the correct service method 22 on receiving a request 6 2 Purpose Services are needed by the client application for two main reasons The first of these is to act as a proxy between the data store and the application handling requests loading the correct data files searching tweet indices parsing query results and constructing appropriate responses The complexity involved in managing these tasks is abstracted by defining a simple request re sponse model The client can make a request to a service for a particular type of data without having any knowledge of the process undertaken to actually generate or obtain that data It only needs to know how to parse the response it receives The service itself is built entirely in Java making the organisation of such a process considerably easier than if implemented solely by the client The second purpose of services is to support client side components in performing d
141. his complexity by selecting an update mechanism using the request type as the determining factor 69 Hashtag Queries Figure 5 12 illustrates the update mechanism used during a single hashtag search E l Components i arse results H updated Figure 5 12 Hashtag Search Update Mechanism onvert Response lo send update signal to components Update query cache with response Single Response a The response from the server is first cast to a SingleResponse and set as the current response within the component manager The query cache configured earlier is then updated with the server response If the response has any results these are then parsed into tweets from the JSON entity returned using ClientTweetParser described in Section 5 6 9 Finally the update signal is sent to all components in turn where each component then performs manipulation and presentation of the data as defined by its update method Slashtag Queries Figure 5 13 illustrates the update mechanism used during a slashtag dual hashtag search Convert DuoResponse to 2 SingleResponse Sand update Convert signal to H OuoRequest to 2 e components Single Requests Convert Response to DuoResponse Update query cache with responses Parse both result sets Display errar Figure 5 13 Slashtag Search Update Mechanism Slashtag requests require more management due to the possibility that both one or
142. his method was shown in Table 5 22 Tweets Classified English Non English ASCII Non English Non English ASCII ASCII Non ASCII Table 5 22 Language Classification ASCII Non ASCI English 96 25 of all English tweets contained non ASCII characters whilst 31 of non English tweets con tained only ASCII valid characters This method of filtering would remove a large proportion of English content as well as continued analysis of much non English content Based on these results this method was quickly discounted Another approach tried was to use a text classification library The Java Text Categorizing Library JTCL REF was used to try and guess the language of each tweet JTCL is based on the algorithm proposed by Cavnar et al 46 whereby an N gram frequency profile is computed for each tweet which is compared to known profiles for various languages using a distance measure Whilst very accurate for documents as small as 300 bytes tweets pose a challenge in being a maximum of 140 characters even shorter after removal of links user mentions and retweet markers Table 5 23 shows the results from using the JCTL library Tweets Classified Language Match Language Mismatch 100 Table 5 23 Language Classification JTCL The JCTL generally managed to identify language correctly with the exception of tweets which featured several hashtags Though all punctuation is removed hashtags are still textually strange oft
143. ially additional development work has been clearly identified which 1f com pleted would further elevate user perceptions of Pulse In the final chapter we conclude with a discussion of whether Pulse has achieved its aims chal lenges and triumphes faced in developing Pulse and future work that would improve functionality usability and desirability 142 Chapter 8 Conclusion This final chapter provides a summary of aims achieved and suggests future work which could be carried out to improve and extend Pulse 8 1 Summary The aim of this project was to develop a social media analysis toolkit which would allow in depth analysis of Twitter data through an engaging user application Accomplishing this aim involved the development of two distinct subsystems The first was an application to manage the process of gathering data from Twitter In addition to streaming parsing writing and indexing tweets on a rigid schedule this application had to be robust and stable able to run autonomously for long periods of time Several technologies were tried to determine which would best handle the storage and retrieval required after which the Terrier information retrieval platform was selected Every day the data processing application downloaded up to 10 million tweets each of which was passed through a pipeline of processing stages before being indexed by Terrier In addition to data management Pulse also had to provide an engaging user appl
144. iating a new search for that tag QueryTag ery Tag Figure 5 18 Tagged With Component Purpose The component should provide context about the query tag informing users of tags used in tandem with their query and in what proportions This should be done in a visually compelling way that makes clear the most and least popular tags used in conjunction with the query tag 75 It should also aid results exploration allowing users to organically move from one query to the next by investigating links between particular hashtags Allowing users to move from query to query as a natural process instead of always using explicit searches enables Pulse to act as a discovery tool as well as providing information on explicit queries Finally it should clearly show associations between particular hashtags By providing these associ ations users should be able to expand their query to hashtags they may not have otherwise searched for Motivation Visualising co occurring tags is useful for understanding tag usage context This 1s especially appli cable in fields such as brand management for example advertisers may wish to know what tags Twitter users are associating with a brand name If users are tweeting negative tags for example Fbroken or fail in conjunction with a brand then action can be taken to identify the reasons for user dissatisfaction and target those users Usage of this technique is mentioned by H
145. ication through which tweets could be queried visualised and analysed Evaluation of existing systems revealed that most focused on the immediate present and they rarely incorporated more than one specific functionality Pulse is different in providing data for short term medium term and long term queries Tweets can be observed on an hour by hour basis to identify first mention of a topic or over the course of months to visualise usage patterns and identify trending topics Few of the evaluated systems offer such range Pulse also acts as a dashboard for Twitter analysis Components unify functionality which had previously only been available through standalone tools Every query type results in a cohesive suite of components each with a specific motivation and purpose In many cases Pulse provides more features than the equivalent tool in the standalone systems evaluated particular in discovering 143 trends and understand global Twitter usage In this respect Pulse is unique amongst Twitter analysis tools The user application is also extensible New components can be rapidly developed owing to the highly modularised application structure with the layered application architecture abstracting com plexity between different each layer Having developed Pulse an empirical user evaluation was conducted with a mixed ability participant group The evaluation aimed to establish two things whether the application was usable and whether it
146. ices folder Any original hourly indices not merged in the first round are then ignored Though now the application always generates 24 hourly indices previous development versions occasionally failed to create an index This failure was compounded through another index being 47 Test Input Expected Output Result De vee Application run with valid Streaming begins Success application runs 2 Application run missing Application usage error dis Success message dis 3 App startup with previ Resume streaming through Success streaming re ously used data folder app recovery sumes from correct hour day 4 200 OK connection re Begin reading tweets from Success tweets streamed Moo 5 Non 200 connection Exponential wait for every Success wait applied error S mpanse O tern expe logged oo Non null response read Parse tweet Success tweet parsed from stream 7 Null response read from Linear wait applied until Success wait applied until Fea S DO Pera onl response reed Valid tweet parsed suc Date checked relevant trig Success triggers fired a cessfully based on date Invalid tweet parsed unsuc Discarded add to fail count Success tweet discarded Plenty pe 10 Tweet date equal to or after All triggers reset new dir Success dir struct cre ee struct created ated new triggers created 12 Tweet date equal to or after Indexing pipeline activated Success indexers added hour trigger
147. ided an unprecedented opportunity to observe how different types of user interacted with Pulse in directed and undirected scenarios The evaluation was structured to mea sure the system against three criteria e Usability all of the functionality in Pulse must be easily accessible by the end user enabling them to achieve their goals and be productive e Desirability the application should be desirable encouraging high user engagement satis faction entertainment and ideally fun e Usefulness the information visualisations and analysis supplied by Pulse must result in the user understanding a particular facet of Twitter in a meaningful way after usage 7 2 Evaluation Preparation With the aims of the evaluation defined an empirical procedure was required to measure the success of the system against each aim This involved selecting appropriate techniques to elicit information from participants designing a task list which steadily introduced new concepts whilst maintaining user interest and obtaining pertinent comprehensive feedback from each participant detailing their opinions of the system 124 7 2 1 Pre Evaluation The pre evaluation questionnaire was designed to establish the prior experience of each participant in areas related to Pulse This can then be used in conjunction with evaluatory results to understand usability and desirability relative to each participant s social media experience Three main areas were consid
148. igham et al 50 achieved using Skyttle 32 a brand management tool which identifies influential web users across many social mediums in specific topic areas Data Requirements The component requires the full hashtag list for each tweet in the result set The query cache used by this component stores the generated tag cloud as a FlowPanel equivalent to an HTML lt div gt section as well as a hashmap of tag occurence pairs This is used to generate the comparison table used by the Duo version of the component Cloud Generation The algorithm used to generate the tag cloud is shown below in pseudocode for all tweets if tweet has tags if tweet has unique tag store new tag entry set occurences to 1 else update tag entry set occurences to occurencestl get min max occuring tags ditt max min Le are 3 10 numSizeSteps 1 the number of font sizes to use else numSizeSteps diff 10 add query tag with num occurences set initial ont size 76 for each size step starts at max size step reduced by size step on loop for each tag 1f tag occurences gt sizeStep add tag num occurences mouse handlers remove tag from tag list reduce font size All tags that feature with the query tag are counted and stored as key value pairs The difference 1s calculated and if the difference 10 a number of size steps are iterated over If the tag has more occurences than max occurring tag size step it is added to the cl
149. ike Google slightly cluttered mm mowes ooo Table 7 4 Verbal Protocol Analysis as a whole offered Typical comments would be surprise or intrigue at features as they were intro duced throughout the evaluation This can be seen in the type of words picked by each participant group Novice users focused on more abstract terms creative impressive fresh whilst more experienced users honed in on particular system features they liked or disliked simple to use powerful inadequate 7 4 2 Usability A standard usability questionnaire based on the IBM Computer User Satisfaction Questionnaire 53 was used to quantatively judge the usability of the system Each participant was presented with 14 statements with an associated Likert scales and asked to rank their agreement with each statement these statements are provided inline with the analysis below Due to varying user interpretation of the distances between each value on the Likert scales and hence the relative meaninglessness of average values the aggregated scores were transformed into box plots for analysis The aggregated results of questions through 7 are shown in Figure 7 12 Statement 1 Overall I am satisfied with how easy it was to use Pulse had a median value of 2 with the majority of participants ranking easy of use between 2 and 3 This implies moderate agreement with the statement and validates both the find
150. ime queries Scenario 9 What is the most popular device used for tweeting in February after the actual Twitter site How did this change on 19 February Exploration You ve now completed all of the formal tasks in this evaluation However Pulse allows for exploration of tags trends and topic As such should you wish to use Pulse further with your own queries please feel free to do so Your thoughts and opinions through free use would be greatly appreciated Finished Once you re ready to continue with the post evaluation questionnaire please let the demonstrator know Appendix E Administrator Guide 204 40000 oo oo 00 eecee 06000 400000 060 oo ot 400000 00000 ee 66 66 66 66 eo 0o 4600000 66 00 66 ee6ee 60000 40000 oo 66 40 00000 0606000 oe ee oo oo Go oo T 000000 O00000 1400000 000000 T 40006 0606000 040000 0606000 Administrator Guide Version Change log INT Added property file info 0 1 Initial draft branched from User Guide Pulse for Administrators Background Pulse is built using the Google Web Toolkit framework GWT supports the building of complex browser based applications which are written in Java then compiled to JavaScript These are then deployed as a Java Serviet WAR for production use Requirements e Pulse WAR Web Archive e Apache Tomcat v7 or greater http tomcat apache org e Pulse Data Source daily data directories e For best results Go
151. in Pulse e Pre Evaluation Questionnaire initial questions to establish participant familiarity with con cepts closely linked to Pulse social network experience Twitter experience and trend anal ysis experience e Evaluation Task List instructions for the evaluation and a series of tasks designed to encour age learning and exploration of the system e Post Evaluation Review questions for users which encourage reflection on their use of Pulse as well as a word list used to perform a guided interview with the participant e Administrator Guide supplementary documentation for administrator s detailing how to con figure a new instance of Pulse These materials are included as appendices to this document 7 2 5 Participant Recruitment Participants from the following demographics were invited to evaluate Pulse e Computing Science Undergraduates other students within the university with a solid techni cal background in computing science e Computing Science Postgraduates graduate level students with a particular focus on infor mation retrieval e Non Computing Science Participants those from other fields with varying levels of Twitter computing knowledge The user evaluation was completed by 7 participants in total 128 7 2 6 Evaluation Sessions Evaluations were carried out in two environments a computing science laboratory at the University of Glasgow and the home of the author In each case two monitors were c
152. ings of the qualitative evaluation and par 138 PostQ Usability Questionnaire Boxplot Q1 7 al ql as a4 as O6 ar 1 j ma dd a EET a a A G adla n iu 4 L A Max Ps X QuartileS Figure 7 12 Result Boxplots Questions 1 7 ticipant comments that work needs to be carried out to improve the application s display approach on lower resolution devices Statement 2 I could effectively complete tasks set using Pulse was included to measure how effectively the tasks supplied could be carried out using the system The median response for this statement was also 2 however the majority of results lay between 1 and 2 with only 1 outlying value of 3 The participant who ranked this task 3 was also the most novice user in the participant group mentioning that at first the component interface was confusing Additional online help as discussed previously could be implemented to improve scores on this matric through generally speaking participants were happy with their task completion effectiveness Statement 3 I could complete tasks set quickly using Pulse measures user satisfaction with the time taken to complete each task From observing participants the main blocker to faster task completion was simply inexperience with the system again further online help to hasten learning and simply more time with the application would likely improve scores for this statement That said the lowest level of agreement here was
153. inish before issuing another The Search Button will be Close disabled during this time left Navigation Bar The Navigation Bar provides options for queries as well as allowing comparisons and restoring cached results Date Query History Compare lags Options Navigation Panel Hashtag Queries Retrieved Results 100 Each of the Navigation Tabs when clicked opens a Navigation Panel Tag Query Navigation Panel The Navigation Panel provides a history and cache of all hashtag queries issued Tag Query History 1 science Tag Query 2 art You can click on any of the queries to load that query s results back google Request 2 into the Component Area This will often be considerably faster than O re querying so utilise this to speed up usage of Pulse Date Sun Mar 06 2011 Req Type HASHTASS ONEY Tf you want to see the options used in calling that query hover over EELS ne ae ld aa the query text a pop up will display detailing information about Used POS Tagger true that query left such as the date searched how many tweets were o oa analysed and so on You can use this information when comparing queries to see how results differ for the same query depending on the options that were set Date Query History Panel The Navigation Panel also provides a history of all date only queries i e tume queries those that don t have any search keywords Query ID Date Query Histo
154. ion and Document classes are easily extended to cover tweets Retrieval Effective Initial tests of Terrier running in conjunction with a simple streaming ness application showed satisfactory performance whilst not hindering the streaming process in any way Required Infras Aside from the Terrier application and available hard disk space tructure none Platform Maturity Terrier is currently in version 3 with a planned further release this year Terrier is widely used within the research community for non trivial search indexing and retrieval projects Additionally the lead Terrier developer is also a project supervisor Comprehensive docu mentation is provided online Table 3 2 Terrier Suitability Analysis 3 2 4 Retrieval Platform Selection The initial version of the application used HBase as a data store for tweets with Terrier providing indexing capabilities However as feared the stability of the cluster was not sufficient for the always on application and so HBase was discarded as a storage option From there Terrier was the obvious choice to power the data processing application in both stor age and retrieval In addition to being highly customisable and scalable coping with millions of documents per index it is also well tested stable and mature 3 3 System Architecture Pulse is comprised of 5 major components The data processing application is responsible for obtaining storing and enabling later tweet r
155. itter outages rate limiting or any other eventuality 1s paramount With the application streaming tweets all day every day it should also operate with minimal user 26 supervision Logs with appropriate messages warnings and errors should be provided to verify application stability troubleshoot errors and preempt more serious failures Integrity checking the data streamed is also necessary at all points in the application This includes validating format from the stream catching malformed content during parsing and ensuring proper and correct storage within the data structures utilised 4 2 Application Design With the requirements above in mind the application went through two major design iterations 1 HBase centric Store HBase used as the central data store for all tweets Retrieval to be through HBase scans or Terrier indices 2 Finalised Terrier centric Application Terrier used as both storage and retrieval mechanism utilising a concurrent indexing pipeline Initial development of the application began in tandem with the client application At this time the primary data store was HBase see Section 3 2 3 for further discussion As a rapidly developed prototype the highest priority objective was on storing tweets in a way that could be retrieved and manipulated later rather than focusing on the concrete detail of how retrieval would be achieved The earliest version of the application was structured as shown in Figure 4 1 E
156. k oo et a de ek A eh ke L 61 29A Navira om Bal pala Es a A a e ERS 62 JI COMPONER ATCA tee gens e heads oa is a ii UE Os 64 5 6 Layer 2 Component Management 65 LOL PULDOSe a gr ds e a E A eee aio ee ES 65 5 62 Update WorkOW es A a e is A A A 65 6 5 63 MARGON 2 2 4424 4 5404 5 eee a goals A 26S BS 2 67 5 6 4 Requests RESPONSES r s 6 price ee ee we eR eS d 68 5 6 5 Component amp Request Types 0 0 00 02 eee eee 68 00 QUE Cahe ys dec OS os a Bh ae Se tees Oh Sa O re ee i 68 5 6 7 Navigation Modification 2 2 ee 69 6 0 Update Mechanism s 2 244400 24 4044 4 oe eh oe aR eS 69 30 9 Tweet PASS sti ee A ene A oo eee ee A eee 12 5 0 10 Component Area i s ysa due E aTa A AA A ole ele A 73 Sf ayer So Components e 2 6 te to Swe OR RE eRe BEG ds ees 73 5 7 1 Component Specification ia we be we be be we ee es 73 5 7 2 Creating New Components a 74 Jhs aged With Component s e 2 8228 da ES ete 75 JIA sbocation Component saaara RR a dA RS SE 78 5 7 5 Tag Volume Component 84 5 7 6 Geographical Heatmap Component 004 85 SLF An Context COMPONEN s ses a a Ree Be ew 88 5 7 8 Sentiment Analysis Component 00000004 91 5 70 Trends Component 66 A 1 546 es Be 97 5 7 AU Place Device Component so porcelana Ee EG Ee A 101 5 7 11 Sentiment Volume Component 2 2 2 000 103 5 12 Retweet Paths Component gc 2 i e of hee e ta we BE
157. ks were obtained using a Java heap size of 1500Mb on a dual core 1 8Ghz processor CentOS 5 machine Index Type Total Time hh mm ss Total rIndex Size Mb 00 35 23 a7 UID Lookup 05 14 04 5289 Text Only Index 00 48 09 782 Table 4 4 Daily Merging Benchmarks N B The vast majority of the UID lookup index size is the meta index Merging Challenges The main difficulty in merging indices was trying to integrate the process into the existing indexing pipeline Merging multiple indices particularly of the size used by Pulse takes a large amount time and resources Running the merging process alone with anything less than 1500Mb of memory often led to heap space exhaustion and during preliminary testing of a unified indexing pipeline similar failures occurred at midnight with even higher heap sizes due to the fact that both hourly and daily indices were being created Ultimately it was decided to run the daily merging process as a seperate task for both merging stability and robustness of the data processing application This process has been scripted to be as smooth as possible but further work is required to fully integrate it into the indexing pipeline Were it to be fully integrated a distributed structure would almost certainly be required to handle the work more so were the application to be scaled further in terms of tweets processed Ensuring merging time was kept to a sane limit was also challenging In order to merge the 24 ho
158. language html March 2011 Terrier 3 0 Javadoc Collection terrier org docs v3 0 javadoc org terrier indexing Collection html March 2011 Terrier 3 0 Javadoc Document terrier org docs v3 0 javadoc org terrier indexing Document html March 2011 Terrier 3 0 Javadoc Interactive Querying http terrier org docs v3 0 Javadoc index html March 2011 TweetMeme http tweetmeme com March 2011 147 38 Twitter QOS Guidelines http dev twitter com pages streaming_api_ conceptstfquality of service March 2011 39 Twitter Vision http twittervision com March 2011 40 W3C CSS Homepage http www w3 org Style CSS March 2011 41 What is a Servlet http java sun com j2ee tutorial 1_3 fcs doc Servlets2 html March 2011 42 YUI Yahoo User Interface Library http developer yahoo com yui March 2011 43 S Baccianella A Esuli and F Sebastiani Sentiwordnet 3 0 An enhanced lexical resource for sentiment analysis and opinion mining In Seventh conference on International Language Resources and Evaluation Malta Retrieved May volume 25 page 2010 2010 44 J Benedek and T Miner Measuring Desirability New methods for evaluating desirability in a usability lab setting Proceedings of Usability Professionals Association Orlando USA 2002 45 Albert Bifet and Eibe Frank Sentiment knowledge discovery in twitter streaming data In Bernhard Pfahringer Geoff Holmes and Achim Hoffmann editors Dis
159. list of all dates queried for using temporal range queries during the current Pulse session Date Query History 1 Sun Mar 06 2011 2 Tue Mar 15 2011 3 Fri Feb 11 2011 Figure 5 8 Date Query History Panel Each temporal range query is listed as an individual date entry This panel serves only as a record of temporal range queries issued this query type 1s not cached so no actions other than review can be performed Dates are added to this panel during update workflow by the component manager Compare Panel Figure 5 9 shows the Compare Panel used to issue tag comparison queries All tag based queries issued during the current Pulse session are listed within each tag selection list Input validation 1s carried out to ensure that comparison is only available once at least two queries have been issued Selecting two tags and clicking Compare will initiate a new cached comparison query sent to the component manager for handling 63 Compare Tags Compare 2 queries Select 2 queries below to Tag 2 Selection List compare their results Tag 1 Selection List Compare Button Figure 5 9 Compare Panel Options Panel Figure 5 10 shows the Options Panel cropped for brevity used to display configurable settings to the user Options Hashtag Queries Retrieved Results 100 Senti Analy 100 Use POS Parser Plotting 100 Pulse Queries Figure 5 10 Options Panel The options available
160. ls you have used how does Pulse compare 1 Much 7 Much better than 2 3 4 5 6 worse than Not existing existing Applicable tools tools L L E L a L a L 2 Ifyou were able to access Pulse on a regular basis what purposes would you primarily use it for if any Understanding particularly topic trends C Tracking events C on Twitter General interest Wi browsiig C ouldn t use it C Comparing Twitter i h 3 Which feature functionality of Pulse did you find most useful 4 Which feature functionality of Pulse do you think needs further work 5 Ifyou could add any additional functionality to Pulse what would it be Appendix D Evaluation Task List 198 Evaluation Tasks Fixed minor typographical errors amended certain searches 0 1 1 Revised after reviewing available data 0 1 Initial draft User Evaluation Introduction Thank you for volunteering to take part in the Pulse user evaluation The following pages detail a series of tasks we would like you to attempt using Pulse All of these are operations we expect users to undertake on a day to day basis There are no incorrect ways of completing any of the tasks Should you have any difficulty completing a task or see any errors which you do not understand feel free to skip the task and move onto the next one Should you have any questions you can ask the demonstrator at any time Points to Consider As you complete t
161. lse has been designed so that system functionality 1s divided logically across these four layers At the lowest level is the Google Web Toolkit GWT 20 application framework As discussed earlier GWT provides a development toolkit for building complex web applications where code is written in Java and compiled into JavaScript Once compiled GWT applications can be deployed on a Java web server as a Web Archive WAR which conforms to the Java Servlet 2 5 specification 24 A full analysis of GWT is given in Chapter 3 however in practical application terms GWT provides A comprehensive library of standards compliant widgets structural panels and application layouts for building applications A mechanism for performing client server communication GWT RPC Java development tools vastly superior to any JavaScript tools available Abstraction layers masking common JavaScript AJAX application complexities such as XML HTTP request and cross browser inconsistencies Quick deployment of highly optimised code onto a compatible Java server 55 GWT organises the application in the browser window using a Layout Panel Layout Panels are standards compliant content areas used to structure application layout at the highest level More specifically Pulse uses the DockLayoutPanel to position three content areas shown in Figure 5 2 This is then attached to the RootLayoutPanel a singleton implementation of LayoutPanel which itself 1s atta
162. lysis websites or tools have you used for topic analysis follower comparison hashtag monitoring etc Twitter Search dere C Analytic ly O Twitalyzer C Radiant 6 LJ Twitterurly C Other please specify O 5 Of these tools what features were most useful 6 Of these tools what features were least useful 7 Did any of the tools lack a feature you would have liked and if so what Twitter Experience 1 Do you have a Twitter account Yes C No C 2 What do you primarily use Twitter for select as many as required Posting own Tweets Following likeminded Twitter users Following organisations news a a L Following celebrities O media personalities Research academic a purposes Have account but do a not use Linked to blog website a other social media 3 What platform application do you typically use to post on Twitter Application From Twitter com Official Twitter Application 3 Party Twitter Application Through another website blog etc o oO O O Platform In browser C Mobile Phone C Standalone Application C 4 How many users do you follow and how many follow you rough numbers are O K 5 How often do you post updates to Twitter Multiple times a day O Daily O Multiple times a week L L L L L Weekly Monthly Rarely Never 6 How do you primarily choose to follow new users Friends colleagues Through work Hobbies inter
163. manager e Applying Terrier properties when retrieving results from the hashtag index the level or type of matching and ranking is not important all that matters is that the result set includes every Terrier document which the tag appears in The term pipelines property is left blank to ensure no stemming or stopword removal is applied e Executing the query Terrier executes the query and provides a set of results incorporating Terrier document IDs e Extracting meta data for each Terrier document ID returned the appropriate data either tweet UID or JSON tweet is extracted from the meta index explained in Table 4 3 This data is then returned to the tag query manager Each instance of TerrierSearch is kept for as long as the index it contains is relevant Should a user query for multiple topics on the same date consecutively the index only needs to be loaded once This speeds up round trip query response time quite significantly as will be discussed in Section 6 4 4 If the query date changes the TerrierSearch instances are reset and new indices are loaded The result from the first Terrier query if any is a list of tweet UIDs Should no results have been found the query manager will return an empty response to TwitterQueryServiceImpl Otherwise the tweet UIDs are filtered based on the Retrieval user option which specifies the percentage of results to return 115 eae ald diiniita aia Execute Terrier pes e
164. mentations for each service are discussed in Chapter 6 5 5 2 Application UI Structure Figure 5 4 shows the general structure of the application In line with the design goals of the application the basic structure is as minimalistic as possible Core system functionality is immediately obvious in the Query Bar whilst advanced options are hidden within the Options Panel of the Navigation Bar until selected 60 Navigation Bar Component Area Figure 5 4 Application UI Structure Most of the screen space is dedicated to the component area only populated once a query has been issued Given the number of options queries and features Pulse offers it was important to ensure the user wasn t intimidated at first glance by the interface Options are set to sensible defaults so that users get interesting results from their very first query with focus placed on the component area by fading in components before they display results The remainder of this section explains each unit of the UI in detail 5 5 3 Query Bar The Query Bar shown in Figure 5 5 is the top panel of the user interface allowing the user to submit queries to Pulse for processing by the component manager It is also responsible for forming requests based on user input and passing these to the component manager Date Picker Sun Mar 06 2011 Search Button e ad Figure 5 5 Query Bar The Query Bar features three main input widgets e Q
165. midating These scenarios are inevitable when using a subject evaluation technique but given more time the inter view would have included a request for clarification from participants when they used a term that several others had completely disagreed with Table 7 3 shows the results of the filtering process narrowing down words selected by each partic ipant to the 5 most relevant Le Total Positive 25 71 4 Total Negative 10 28 6 Engaging Simple to Use New Hi Table 7 3 Filtered Word Selection Frequency Forcing participants to pick only 5 words resulted in some interesting changes to the frequency table Impressive still features as the one of the most common adjectives with participants com menting that the system was well designed really responsive and that it had a huge number of features lots of interesting stuff The participant with the most social media analysis experience made some interesing points during the guided interview One of the filtered words they chose to use was inadequate in that Twitter user information is not represented in any way by the system Whilst this was initially part of the design of Pulse it became clear that trying to cover topics time and users would simply be impossible given the project deadline However the fact that an experienced user of analysis tools has considered this an important omission suggests the triumvirate of users topics and time is
166. mponents Pulse was designed to be easy to extend and modify As such the process of creating new compo nents 1s simple 74 e Extend NewComponent components are based on the NewComponent class which should be extended by all components The NewComponent class is discussed in 5 7 1 e Create a component cache creating a component cache and a method which can use this cache to build a new instance of the component allows for fast display of previously queried terms e Define new ComponentType a new enumeration type should be created for the component used as an internal name e Assign ComponentType to RequestType the new component type should be added to one or more request type declarations indicating what types of requests should generate the compo nent e Add rule to NewComponentManager the component manager needs to know what initiali sation is required each time a component of that type is instantiated e g supporting libraries or server side services To do this a new rule should be added to the component manager createComponents method 5 7 3 Tagged With Component The Tagged With Component illustrated in Figure 5 18 shows hashtags that co occur with the query tag in each of the tweets in a result set It displays terms as a word cloud with more frequently co occurring tags drawn using a larger text size with tags getting progressively smaller the less frequently they appears These tags can be clicked on init
167. n has likely been closed Assuming this is a temporary error the application will attempt to reconnect after an appro priate wait time Should the connection end for any reason an appropriate time is waited explained in Section 4 5 1 before reconnection is attempted dependent on the disconnection reason 4 5 3 Writing Streamed Tweets The streaming process will continue as long as a valid connection remains open to Twitter However at certain trigger points the application will send all tweets in memory to file The streaming trigger is initially configured for one minute after application startup Once the current tweet publication date is equal to or greater than the trigger three actions are performed a new TweetWriter thread is created the current TweetWriter thread is passed the tweets it needs to write and the streaming trigger is pushed forward one minute At this point all tweets in memory will be written to a minute file within the appropriate hourly and daily directories 4 5 4 Streaming Issues It was observed that almost exactly once per hour every hour the connection to Twitter was dropped signified by a reconnect message within the application log files Though this only resulted in a near negligible number of dropped tweets roughly 1 or 2 per reconnect it was still occurring even though the implementation meets the guidelines laid out by Twitter As the effect on the application was marginal little time was
168. n is available Pulse Initialising APIs This requires an active internet connection Please wait GWT Languages API Initialised Map API Initialised Home The Home screen is the first you ll see when Pulse has loaded From here you can make queries change options compare queries view saved queries and more Query Bar Navigation Bar Component Area The Query Bar is where you can enter new queries to search for and select dates to search The Navigation Bar allows you review past queries select queries to compare and configure options for further queries The Component Area is where results will be displayed in the form of components 8 Understanding the purpose of each of these areas is vital for using Pulse to its fullest Query Bar The Query Bar is the main location querying Pulse You can enter several different types of query in the Query Box each of which provides access to a different set of information about a given topic or query You can also select a date by clicking on the Date Picker This will restrict results to the date selected Clicking the Date Picker will open the calendar view right You can browse months and select a particular date using this or click Close to return to the Pulse Home screen Finally the Search Button sends your query to Pulse for Go processing Once a query has been started you need to wait 6 7 8 for it to f
169. n the desirable response limit as stated in Section 6 4 1 6 5 Sentiment Analysis Service The Sentiment Analysis Service provides assistance in analysing the sentiment of each tweet in a result set It is invoked by the Sentiment Analysis component once the initial result set from the Tweet Fetch service has been received The approaches considered analysis requirements and implementation detail are are discussed fully in Section 5 7 8 6 6 Deployment Deploying Pulse for production use is a straightforward process GWT automatically generates a Web Application Archive WAR which can be easily launched on a Java webserver such as Apache Tomcat The WAR directory contains both the client JavaScript and server classes libraries required to run the application as well as all images and CSS stylesheets Certain filepaths must be defined for Pulse so that it can locate the data directory and other required files These are configured using a Pulse properties file pulse properties This file is stored within 122 the WAR directory and should be reviewed before deployment On intiailisation each service will read the properties and configure itself based on the property values The three properties currently used are listed below e pulse data dir path to the Pulse data directory Folder names in the path should not contain any spaces e sentiment dictionary file path to the sentiment analysis dictionary required by the Sentiment Analysis
170. nd selecting a date The Options panel can be used to amend more specific configuration settings The results of temporal range queries are displaying using the following components e Trends Component a pivot chart which displays the most popular tags for multiple days in a highly customisable way Tags can be ordered and tracked via numerous variables and displayed using many different visualisation techniques e Place Device Component shows which devices were used most often to tweet with as well as the most actively tweeting countries across multiple days e Sentiment Volume Component highlights daily Twitter sentiment in addition to daily tweet volume e Retweet Paths Component displays the top retweets for a selected day along with a map showing how they propagated across the world e Popular Links Component lists the most popular links from Twitter on the selected day in a visually appealing way 5 4 4 Cached Queries Both hashtag and slashtag queries are cached so that they can restored later without making a server request Because of this all tag based queries can also be compared using the Compare Panel see Section 5 5 4 creating a slashtag view of two cached query tags 58 5 5 Layer 1 Application Framework The application framework structures the user interface into logical areas handles most user input defines the client side elements of GWT services and provides meta functionality applicable to all
171. neither query tag may return results Furthermore each part of the query needs to be stored in the cache as an individual request response pair to allow flexibility when selecting queries for comparison from the query cache To do this this response from the server a DuoResponse needs to be split into two SingleResponse instances Each of the two entries in the query cache is then updated with the appropriate response Should either of the query tags return no results then an information dialog is displayed and the update mechanism changes to that of a single hashtag search Similarly should neither query tag return any results then an error is displayed If both query tags have results then these are parsed and the update signal is sent to each component in turn Once all components have been updated the query cache for each tag is finalised converting 70 the DuoRequest into two SingleRequests and storing them appropriately Cached Hashtag Queries Figure 5 14 illustrates the update mechanism when restoring results from a cached hashtag query Cache query Retrieve set global request request sent by cache from and response to send update signal using cache Figure 5 14 Cached Hashtag Search Update Mechanism 3 no results gt Yes 07 Navigation Bar Tag cache cached request Query History manager responsa Cached update requests are always sent from the Navigation Bar either by clicking on a tag in the
172. no results found error will be displayed below Pulse Mo results were found for ukr If there are any results the component area will now contain the hashtag query result components Additionally these results will be cached in the Tag Query Navigation Panel Hashtag Query Components Successful hashtag queries will result in six components being displayed inside the Component Area 13 ees C 3 bs oo Location Component Cy Sentiment Analysis Component Tweeting Users Followers vs Following MTweets a E I OPES Tagged With Component Tag Volume Geographical Component In Context Heatmap Component Component Tagged With Component The Tagged With Component displays a word cloud showing tags that were used in conjunction with the query tag Query Tag E Associated Tags The Query Tag shows how many times that tag was mentioned in brackets The size of all other tags is relative to the number of times they appear within the results again displayed in brackets Hover over any tag to highlight it and click the tag to automatically query for that tag Note Tags that appear only once in conjunction with the query tag are not displayed 14 cP Location Component The Location Component displays a map of markers each corresponding to one tweet Map Marker Map Controls The Map Controls can be used to l sic 00 pan and zoom into out o
173. ns are found and reduced by 0 5 if any negative emoticons are found The final score is then banded into one of six categories very positive score gt 0 5 moderately positive 0 25 lt score lt 0 5 slightly positive 0 lt score lt 0 25 slightly negative 0 25 lt score lt 0 moderately negative 0 5 lt score lt 0 25 or very negative score lt 0 5 This sum of tweets within each band is returned to the client along with a small sample of tweets within each category This process is performed in real time however the POS tagger takes 50ms per tweet to identify parts of speech For this reason a less accurate option was introduced Users can opt to bypass the POS stage and instead each word is looked up with every possible POS type appended to it noun 93 verb adjective and adverb If there is more than hit in the dictionary an average score is taken whilst less accurate it gives users the option to analyse sentiment faster Data Requirements The Sentiment Analysis component uses the text of each tweet for analysis The query cache used by this component stores the total number of tweets in each sentiment cate gory as well as a small sample of tweets from each category Data Preparation amp Visualisation Sentiment analysis requires several external files and libraries to operate To achieve this a service is invoked by the component SentimentAnalysisServic
174. ntiment Clicking on a segment of the chart will display an information window with further details about how many tweets were categorised in that band and what percentage that was of all tweets Samples Tab The Samples Tab shows a small sample of tweets Very Positive wow the USA is the new china blocking access to nytimes amp more that contains wikileaks E a n d th e Ca teg O ry th ey we re p a ced l n material can t even trend wikileaks Love it ATM MY IN everything MANNY I EE EE ic ARS IE A E oe MS E yor A ER government twikileaks RT eevblog Michael Moore posts bail for Julian Assange Awesome work MMEFIint http bit ly i0pLDg wikileaks 1 y I 1 I There is no real legal case there are just two totally fucked doubly groupies a Sam p es fro m ve ry po S itive to ve ry n eg ative http is gd iL6KV wikileaks RT medienfloh wikileaks mirror installiert OK Jetzt hei t es abwarten und Club Mate trinken RT EFF Constructive Direct Action Against Censorship https eff org r v7q WikiLeaks netfreedom Use the scroll bar on the right hand side to view Moderately Positive RT antloewenstein RT We have been living in a New Dark Age Wikileaks is turning on the lights No wonder the rats are scurrying vi Details Tab The Details Tab shows exact numbers of Total Tweets Analysed 343 Total Categorised 2 77 81 0 tweets analysed the relative rankings of Very Mo
175. nts Development of the Pulse client application was focused on three user interaction concepts which represent the main actions users should be able to perform using the system e Query users should be able to issue advanced queries covering both topics and time with advanced configurable options e Analyse users should be assisted in the analysis of Twitter data using visualisation tech niques filtering and aggregated statistics e Manipulate users should be able to manipulate data beyond what is initially presented both in single units of functionality and across the whole system From these interaction concepts concrete evaluable user requirements were established Most im portantly users must be able to immediately query the system in an intuitive way The interface should clearly indicate what is required of the user and should model typical website user interac tion where appropriate a2 Multiple query types must be available to allow users to search based on both particular topics of interest and over periods of time with results providing a comprehensive view of the query domain Switching between query types should be implicit based on user input rather than an explicit choice Components logical units giving a particular perspective on the result set should give a broad informative overview on initial response from which users can investigate further by combining queries or by drilling down for mor
176. number of tweets per hour is determined by iterating over the result set and checking the publication time against each period by default there are 24 periods of 1 hour This is then transformed into a data table supplied to the Visualisations API constructor which generates a new column graph 84 Component Testing Table 5 10 shows the test cases defined for the Tag Volume Component Test Case ID Hashtag query results are returned Slashtag query is converted to hashtag query due to 1 query tag having no results Cached query is selected Bar in the result graph 1s clicked Table 5 10 Test Cases Tag Volume Component Table 5 11 shows the tests derived from each test case and the results of running each test A hashtag query with re Graph of hourly tweet oc Success component dis sults curences for given tag over plays volume graph day 2 A slashtag query where Output of test case 1 Success component dis only 1 tag returns results plays volume graph A cached hashtag query Output of test case 1 Success component dis plays volume graph Click on any bar within the Info window detailing Success info window dis result graph time period and number of plays time period informa tweets Table 5 11 Tests Tag Volume Component Duo Equivalent The Duo version of the component displays the aggregated periodic volumes of each result set as pairs of columns for each time period 5 7 6 Geographical Hea
177. of data reduced each dailyStatsTop10 log file 18 around 30Kb much more man ageable for real time use When a request is received the server side application loads the appro priate dailyStatsTop10 log file and parses the DailyStats object into a serializable format Usage of these files 1s discussed in Chapter 6 4 10 Testing Table 4 8 describes the test cases relevant to the streaming component of the application Test cases 9 focus on the robustness of the application whilst cases 10 12 primarily concern synchronisation and propagation of triggers to other components within the application Table 4 9 shows the actual tests performed upon the system based on the test cases with the test results 46 Test Case ID Description Application is run with valid arguments data folder path Terrier home path Previous run of application ended unexpectedly same data folder is pro vided 3 HTTP response code from Twitter API connection 200 om S Tweetparseis successful SSS 9 Tweet parse isunsuccessful SS Table 4 8 Test Cases Streaming These tests were carried out numerous times over the course of development with baseline results stored for comparison against results taken after new functionality had been added for example the failure recovery scope changing from daily to hourly level recovery Table 4 10 describes the test cases relevant to the indexing component of the application Terrier was trea
178. ogle Chrome see Compatibility Installation Local 1 Ensure you have downloaded the Apache Tomcat server You can download this for free at http tomcat apache org download 70 cgI ae 2 Once you have installed Tomcat copy the Pulse WAR pulse into Tomcat s webapps directory 3 Modify the pulse properties file in pulse WEB INF o pulse data dir should have the path to the Pulse data folder o sentiment dictionary file should have the path to the sentiment dictionary file provided pulse WEB INF dictionary o sentiment pos tagger file should have the path to the sentiment POS tagger file provided pulse WEB INF tag tagger 4 Start Tomcat using the startup script for your OS usually lt TOMCAT HOME gt bin startup bat or sh o You may have to set an environment variable in your OS for Tomcat JAVA_HOME or JRE_HOME pointing to your Java installation o Additionally setting CATALINA_OPTS to Xmx1500m will set the Java heap to 1 5Gb resulting in a faster application experience Adjust the number based on your system spec 5 Access http localhost 8080 pulse 6 Pulse is now ready to use Additional Deployment Steps Operation outside of localhost requires a valid Google Maps API key 2 1 Sign up for a key for your domain at http code google com apis maps signup html 2 Amend line 10 of pulse Pandemos html from this lt script type t
179. omatically adjust to an appropriate zoom level so that the tags can be better compared and visualised over time An excellent example of this would be to compare usage of tags such as Fegypt bahrain tunisia libya and yemen over a temporal range of two months The Trends Component would show spikes in user overlaps between each when the tag first and last appeared and how widespread tag usage was None of the tools evaluated provide the flexibility and customisation available with this component Data Requirements This component uses the top hashtags hashmap returned by the server in response to temporal range queries The tag hashmap contains string keys representing the Unix timestamp for each day returned and a comma separated string value with each tag and its associated variables The structure of each value is shown below tag lt numOccurences gt lt numUniqueUsers gt lt numUniqueCountries gt Data Preparation amp Visualisation The GWT MotionChart widget 18 powering the Trends Component visualisation must be supplied with data in a specific manner to enable pivoting functionality This is shown in pseudocode below dataTable new DataTable GWT visualisation structure dayTagLimit request getTagLimit max top tags to show per day for each column required each column is one variable countries dataTable addColumn unique users occurences etc sort hashmap keys each key is a
180. ommon to all components This can be called by the component manager at any time with guaranteed results These methods are used to control 73 Widget Area Component Information Presentation Figure 5 17 Component Structure presentation of components in exceptional circumstances should component specific handlers not be defined e Loading a generic loader image 1s displayed whilst results are being retrieved or computed e No Results Found displays a simple error message within the component when queries return no results Though these methods provide a fallback should they not be extended by a concrete component certain functionality must be defined for components to function correctly within Pulse These methods define what to do with result sets returned how to handle cached updates and how to clear the component e Update The core function of a component is to manipulate and present data in some way after each query The update method should implement the necessary transformations and visualisations e Cached Update How to handle data returned not from a query but from the query cache Depending on what the component stores in the query cache this may differ greatly from a standard update e Clear Components may have to reset certain properties or processes in addition to simply removing data from the widget area The clear method should be implemented to bring that component to a ready state 5 7 2 Creating New Co
181. omponent ScheduledMapFeich ScheduledMapFetch Within Timer run method If queue gt 0 create new Ex ScheduledMapFetch tendedLocationCallback with reference to map tweet text and marker to use if Duo Make asynchronous call to geocoding service ScheduledMapFetch On geocoder return Pick most relevant geocode result create ExtendedLocationCallback info window with tweet text plot on map Table 5 5 Geocode Process Options The only Location Component option is Plotting This is a value between 0 and 100 representing how many tweets from the retrieved result set should be geocoded This can be adjusted for queries which are likely to return a large number of results where not all need to be mapped Component Testing Table 5 6 shows the test cases defined for the Location Component 4 Slashtag query is converted to hashtag query due to 1 query tag having no results 16 Cached query is selected with no map markes 8 Component is geocoding existing query results when new query is issued Table 5 6 Test Cases Location Component Table 5 7 shows the tests derived from each test case and the results of running each test 81 Test Input ID 1 2 3 5 A hashtag query with re sults map plot option set to 100 A hashtag query with re sults map plot option set to O A hashtag query with re sults map plot option set to between 1 and 99 Slashtag query where only l tag returns r
182. on polling The Sentiment Analysis component should be able to answer questions related to these topics For example a user may be considering purchase of a CD by a band they ve recently heard of say royksopp To help make their decision they opt to search Pulse and see 91 what other users are saying about the CD The Sentiment Analysis Component will categorise each tweet returned as either positive The new royksopp album is great or negative very disappointed by royksopp album The aggregated results will then be displayed to the user along with some samples from each category to help the user make a decision Another example usage is in opinion polling Take two political parties the blues and the reds How does public opinion towards each differ The Sentiment Analysis Component can evaluate statements featuring each tag and provide a comparison of each as well as sample tweets for each category Approach Numerous approaches were attempted before a final process for sentiment analysis was decided upon The following factors were considered for each approach e Performance individual tweets are analysed for sentiment in real time on retrieval from the Terrier index As such the time and processing power required to perform sentiment analysis must be bounded to a reasonable limit e Accuracy the method used to analyse tweets for sentiment must give a reasonable level of accuracy and
183. ond result set in which to display the tweet characteristic statistics and plots the follower following data for both sets as two colours on a scatter chart 90 Expected Output Result A hashtag query with re Scatter chart of follower Success scatter chart sults following pairs aggregated characteristics panel dis tweet characteristics in new played tab A slashtag query where Output of test case 1 Success scatter chart only 1 tag returns results characteristics panel dis played A cached hashtag query Output of test case 1 Success scatter chart characteristics panel dis played Table 5 17 Tests In Context Component Future Development At the moment Pulse does not utilise individual user information to any great degree This com ponent could be used to display a social network connection graph for each tag query with links between each tweeting users visualising the clusters of connected tag usage 5 7 8 Sentiment Analysis Component The Sentiment Analysis Component shown in Figure 5 24 processes tweet text and tries to cate gorise it into one of six sentiment groups from very positive to very negative Tweet Sentiment towards science Very Positive if Total Tweets Analysed 110 Total Categorised 94 85 New biology digestive system iphone education app accepted and ready Very Moderately Slightly Slightly Moderately Very http bit ly biodigsys anatomy science Posi
184. onfigured on a single machine providing each user with the best possible visual experience Though this is arguably misrepresentative of Pulse were it to be deployed as it stands the need for further low resolution optimisation has already been documented as a necesary future development Before the evaluation began participants were briefed on the system and given the opportunity to ask any questions The evaluation process was also explained Evaluations were estimated to take 45 minutes though in practice this varied based on user engage ment post evaluation discussion and interest in the system 7 3 Pre Evaluation Results Analysis 7 3 1 Social Networking Experience The first series of questions were designed to establish each participants general social networking experience Figure 7 3 1 illustrates the variety of social networks used by participants PreQ SN Q1 Social Networks Used E Social networks used A A Ai mall tT Al mi 0 sb Figure 7 1 Social Networking Experience All participants used Facebook unsurprising given 1ts ubiquity amongst university students In addition 3 participants used 4 or more different social networks including Twitter suggesting each may have a particular role or function or fulful a different need within the participants usage of the social web Most importantly all participants had an understanding of the core concepts involved in social networking relationships shared cont
185. oud with the current tag font style and removed from the tag list several font styles are defined in CSS for each iteration and each iteration the style is changed to a smaller font size To ensure clarity tags that feature only once across all tweets are ignored The final tag cloud 1s stored as a FlowPanel Component Testing Table 5 1 shows the test cases defined for the Tagged With Component Test Case ID Hashtag query results are returned Hashtag query results have no associated hashtags Slashtag query is converted to hashtag query due to 1 query tag having no results Cached query is selected Tag from the result cloud is clicked E Tag from the result cloud is hovered on Table 5 1 Test Cases Tagged With Component Table 5 2 shows the tests derived from each test case and the results of running each test Duo Equivalent The Duo version of the Tagged With Component allows for comparison of two word clouds from separate queries shown in Figure 5 19 As well as providing all of the functionality of the standard Tagged With Component the Duo version provides a list of tags which appear in both result sets and in what volumes this can be generated from cache using the tag occurences hashmap The tag cloud container also features a vertical bar which can be dragged left or right to focus fully on either cloud This additional functionality resulted in four new test cases shown in Table 5 3 These were implemented by
186. ows developers to write code in Java which on deployment is compiled to highly optimised JavaScript The compiler supports a significant subset of the Java language with the obvious exclusion of multithreading and concurrency and can create different application code permutations aimed specifically at particular browsers This approach would significantly reduce the effort required in ensuring Pulse is portable across platforms and browsers Furthermore Google supplies Java wrappers for several of its most popular JavaScript APIs These include the Maps API for mapping and location translation the Visualisations API a suite of charts graphs and other customisable data visualisations and the Translation API for language detection and translation All of these would be immensely useful for representing retrieved data to the end user Although development in GWT is in Java it is still possible to include native JavaScript GWT features a JavaScript Native Interface for calling code written in pure JavaScript analogous to the Java Native Interface for calling code written in C C This means both external libraries and handwritten optimised JavaScript can be arbitrarily mixed with Java Server communication is conducted using remote procedure calls to Java servlets These are defined within GWT as services compiled to conventional Java byte code instead of JavaScript This means GWT applications are often deployed as servlet contain
187. particular day In this view an additional drop down list is visible that allows the selection of a particular day to view Hovering over a country shows the number of tweets posted that originated from that particular country similar to the Geographical Heatmap Component Sentiment Volume Trends Component The Sentiment Volume Trends Component shows daily sentiment across all tweets as well Switch to Sentiment Trend as daily volume of tweets Switch to Volume Trend Daily Sentiment J Daily Volume J Daily Sentiment of All Tweets Daily Tweet Volume 1 000 000 5 200 000 800 000 5 000 000 I Very Positive W Moderately Positive ES Slightly Positive 400 000 MM Slightly Negative ZAS E Moderately Negative Very Negative 200 000 A Eivor Nagai 4 800 000 Mi Volume 4 600 000 0 4 400 000 A x A o S S S gt 2 x S y ESSE E E SES SS SS 9 R AN xv gt x amp Device Sentiment Chart Daily Volume Chart To changes views select the desired view from the View List in the upper right corner 30 Retweet Paths Component The Retweet Paths Component provides information about the top retweets of a particular day Retweet Stats Retweet Translation Change Day RT ladygonga Dizem que a Lady Gaga copia a Madonna em Express Yourself Na verdade a Restart copia os Telettubies e o Justin Bieber c RT hechosbeliebers El 5
188. perspective it was explicitly deemed usable by 4 participants and even desirable by 1 participant The prevalence of these terms can be seen in Figure 7 11 Furthermore 5 participants described the system as engaging with one remarking that it would take hours to see everything 4 participants called it entertaining and 3 described it as fun 134 Effective Bright creative U Usable a Engaging Meaningful Advanced Jseful Entertaining Impressive Figure 7 10 Adjective Cloud This suggests that the application is more than a data analysis tool able to provide certain services but also an enjoyable user experience that could be used for entertainment purposes as much as task orientated search Other highlights include positive acknowledgement of the aesethetic design attractive and cre ative in particular as well as recognition of the potential uses of the system meaningful and business like Furthermore the innovative aspects of Pulse were noticed by 2 participants la belling it cutting edge The most frequently occuring negative terms busy and cluttered highlight the difficult in pre senting so much information within the confines of a browser window This indicates further work needs to be performed in hiding or abstracting functionality until requested by the user It was suggested by one participant that only one component be shown full si
189. phic hotspots topic opinions and more The user application presents this data as components units of functionality that provide specific analysis of a given property each of which can be modified to suit user requirements and extended Should new requirements arise additional components can be created from scratch and easily integrated into the existing application Education Use Consent I hereby give my permission for this project to be shown to other University of Glasgow students and to be distributed in an electronic format Please note that you are under no obligation to sign this declaration but doing so would help future students Name _9n9nNnNUN9 Signature Acknowledgements I would like to thank my level 4 project advisors Dr Iadh Ounis and Dr Craig Macdonald for their continued support advice and endless patience Contents 1 2 3 Introduction 9 LE Problem Denno ssam aa 4 6 44 6 2 gt 68g BR a de e e a 9 L JMIOUNAUIONTS 6 400 al Si te e a eee Se OS EER 9 LS PUISeSYStena AMIS e ise cf om ke de Oe A oe hem Sp etc ee ee a a a ii ai 10 1 4 Dissertation Outline lt lt a eS 8 sw ASA BR OS hae eee eR 10 Background 12 2 1 Twitter A Microblogging Service 20 ee 12 AEL UNES fod ee Jeg ER DAA Bo AI 12 2E Socal Relat onshipS 45 a OS Bw a aa a 13 2 1 3 Tweet Terminology Usage a e ee ee 13 2 2 Existing Analysis Systems e s e e e e a a a a e e E a L 14 22 TIWIter Sea
190. plex to use on a query by query basis The final approach combines SentiWordNet 43 the Stanford Part Of Speech POS tagger 58 and emoticon filters SentiWordNet is a lexical resource containing scored sets of English words Scores are applied in three categories positivity negativity and objectivity The word sets are each a group of synonyms defined by WordNet 54 a lexical resource of English nouns verbs adjectives and adverbs organized into sets of synonyms each representing a lexicalized concept The SentiWordNet wordset list was used to create a sentiment dictionary Each dictionary key is a single word annotated with it s part of speech for example house n represents the word house which is of type n a noun Keys are associated with a final sentiment value calculated from each of the three individual SentiWordNet scores The Stanford POS tagger identifies the part of speech of each token within tweet text This is ap pended to the word after which the final word pos token is looked up in the sentiment dictionary Should a match be found the base tweet score of 0 is adjusted appropriately adding up to for positive words and subtracting up to 1 for negative words The final score is derived by dividing the intermin score by the number of words analysed The last step involves applying score modifiers based on emoticons tweet score is increased by 0 5 if any positive emotico
191. ponent With additional devel opment time this component would be integrated into the Location Component with a toggle to switch between the two types of map 5 7 7 In Context Component The In Context component shown in Figure 5 23 categorises tweets in the results set based on several factors Twitter functions used popularity of tweeting users and type of content posted From a development perspective it is used to quickly prototype new Pulse component functionality 88 User Data Result Set Stats 00 E Tweeting Users Followers vs Following i Questions 1 6 42 Tweets containing who what where tweets when why how or Menti 58 inion sem user mentions 43 a 28 tweets O username within text Retweets 5 6 7 2 76 I Users tweeting science Tweets that are retweets utilise the RT o tweets shorthand Popular Users 2 99 Tweets that are from popular users gt 5000 o followers e 2 O ma 10 000 15 000 20 000 25 000 20 000 Unique Users o Y Followers a Tweets for unique users excludes 87 3 1 o Figure 5 23 In Context Component Purpose The component also aims to help users identify whether Twitter is used primarily as a social network or a news source for certain topics Kwak et al 52 found that Twitter displays characteristics unlike any other human social network particularly low following follower reciprocity The In Context component graphs each user s follower
192. ponent area until each has successfully initialised until then the system cannot be used Initialisation requires an internet connection as such Pulse can only be used on an internet connected machine The API initialised are as follows e Google Maps API provides mapping and geocoding services to certain components e Google Visualisations API powers the majority of Pulse visualisations graphs charts cer tain types of map e Google Translate API performs natural language detection and translation 67 Whilst GWT provides the JavaScript Native Interface JSNI for incorporating handwritten JavaScript including external JS libraries into Java code it also provides Java wrappers for the most popu lar of its JS APIs With the functionality provided by the GWT wrapped and competitor s native libraries being essentially the same mapping charts and graphs etc the convenience of the GWT wrapped APIs was preferred 5 6 4 Requests amp Responses Interaction with the server is conducted through requests and responses Each time a new query is issued by the user a request is generated of which there are three types Request an abstract class from which other requests are extended SingleRequest for single tag queries and DuoRequest for dual tag queries The Request abstract class stores information about a request common to all query types For example regardless of query type every request has some date filter Similarly
193. presentation is handled by a browser using technologies such as HTML and CSS whilst data manipulation is handled by a server database querying dynamic page generation and use of scripting languages such as PHP Pulse follows this pattern by defining services with which the client can communicate This chapter describes the services required by Pulse conceptually and in implementation terms These services are then tested and benchmarked against a specific set of requirements 6 1 Services Overview Services provide rigidly defined functionality to the client application communicating via GWT RPC Each service on the server is an implementation of a service interface specified by the ap plication framework see Section 5 5 and can be invoked by calling the defined service method through the client side RPC service proxy see Section 5 6 3 Importantly each service is distinct from all other services Though running on the same server they operate completely independently of each other As such there is no overall architecture unifying services only the RPC mechanism unifying each client service proxy and its server implementation Services are also persistent Some server side processing systems such as CGI 57 do not main tain state between operations However once invoked by the client Pulse services maintain state between future calls This is a highly beneficial attribute allowing for implementation of features such as data cac
194. pular The gradient is applied by generating a hexadecimal colour code calculated as follows colours 512 number of colours available difference maxOccurences minOccurences colourIncrease colours difference aboveMin occurences minOccurences ColourVal aboveMi colour ncrease Lt COLOury al lt 256 colourCode colourVal toHexString OOFF if colourCode length lt 6 edge case COLOUrCOde 0 cohbourcode else blueValue 256 256 colourVal toHexString if blueValue length lt 2 edge case blueValue 0 blueValue colourCcode FFU0 tbhlueValue return colourCode The basic concept was to calculate a colour increase value which is multiplied by the number of occurences over the minimum each retweet has From this a hexadecimal colour code is created representative of a retweet s position in the ordered list 106 Clicking a box reveals further information about that retweet including total occurences and num ber of unique countries There is also a button to switch to the component mapping view see Mapping below Should Pulse detect the retweet is non English it will attempt to translate it using the Google Lan guage API 19 If successful a translation is shown in italics below the original text Mapping The location information associated with each retweet can be used to map retweet propagation Pulse implements this using the Maps API geocoding retweet location in
195. queries 5 5 1 GWT Services A GWT RPC service service allows for communication between the client application and a server using RPC Remote Procedure Calls This allows the JavaScript client application to asynchronously request data from a Java service much like an XML HTTP Request in an AJAX web application Figure 5 3 illustrates the class structure of each service adapted from the GWT Anatomy of Ser vices diagram 21 GWT RPC Service Architecture Imported from GWT library Written for Pulse Generated at compile time Translatable Java code Standard Java code run as JavaScript on client run as bytecode on server Figure 5 3 RPC Service Structure Services consist of client side code defining the service interface and server side code implement ing that interface A concrete implementation of each service interface is generated automatically by GWT at compile time Because no implementation exists until compile time services are accessed via RPC service proxy stubs created by the component manager discussed in Section 5 6 3 Each service requires three classes be created e Service Interface this is definitive version of the service as implemented by the server side class and specifies the expected arguments and return type of the service 59 e ServiceAsync Interface based on the Service interface ServiceAsync is used for creating the client RPC proxy stub e Servicelmpl Class
196. r Each presents a particular view of Twitter activity in a unique way by topic by topic comparison or by time 5 4 1 Hashtag Queries Hashtag queries are the simplest query type in Pulse issued by typing a hashtag in the search box and selecting a date to search The resulting components provide an overview of tag usage on the date selected including temporal geographical and sentiment analysis The results of hashtag queries are displayed using the following components Tagged With Component shows the query tag and its volume along with all other tags that co occur in each result tweet e Location Component marks each tweet with a marker on a world map with the tweet text viewable by clicking on the marker e Sentiment Analysis Component provides sentiment analysis of tweets featuring the query tag e Tag Volume Component shows the distribution of tag usage over the course of the date selected e Geographical Heatmap Component shows the total number of tweets per country featuring the query tag e In Context Component provides additional contextual information about the query tag s us age Each of these components is explained in depth in Section 5 7 5 4 2 Slashtag Queries Slashtag queries allow for comparison of two tags on a given day They can be issued by inputting a pair of tags in the search box separated by a slash and selecting a date to search The resulting components provide comparison of results for
197. r accounts that may jeopardize search quality Material that degrades search relevancy or creates a bad search experience for people using Twitter may be permanently removed 38 Whilst this has the potential to show bias towards results in particular application scenarios such as the ratio of spam to non spam tweets I believe for Pulse s purposes it is a negligible issue Users of Pulse will want to understand what real users were discussing and in what way rather than filtering through unimportant or spam results Once these are filtered Twitter performs a modulo 100 operation on each public tweet s UID Those which returns results in the 0 10 range are streamed to Gardenhose users Though this itself is not random Twitter states that this in combination with its status ID assignment algorithm the details of which are undisclosed ultimately tends towards a random sample in non trivial streaming scenarios These conditions may lead to doubt surrounding the true randomness of the sample provided and whether bias particularly towards quality is being introduced However for Pulse s purpose this data set is still incredibly useful for two reasons 1 Twitter offers a vast result set of millions of real time tweets every day along with large amounts of supplementary data Few other social media data sources are as comprehensive concise popular and easily available 2 Pulse end users will primarily focus on quality tweets which
198. r if so the application will attempt to restart from where it failed Firstly the folder is checked to ensure it is valid A valid Pulse data directory will contain only directories with Unix timestamps as names midnight on the day the data applies to Finding a file or any other type of folder will result in the application exiting Once validity is confirmed the latest day folder will be checked If the day matches the current day the application will resume from the next hour of that day So if the application fails at 9 05pm on 24th February and is restarted immediately it will resume streaming from 10 00pm Similarly if the application is restarted at any point during the original day it will start from the top of the next hour up until midnight If the day does not match the current day the application will create a new day folder with the current day s Unix timestamp It will then start streaming immediately into the new day directory Whilst these measures provide broad recovery solutions streaming usually begins again within an hour of failure they could be improved Waiting until the hour means that potentially 59 minutes worth of data is ignored and during testing holes within the aggregate data could be seen from where the system was down However having now reached stability the data application downtime is low and the additional effort of resuming streaming by the minute or even second was exponentially greater
199. r as an XML response JSON is a data interchange format easily parsed by browsers that is considerably more lightweight than alternatives such as XML Ensuring parsing and processing tweets took as little time as possible was crucial in achieving the processing speeds required given the number of tweets being dealt with each second both in the data processing application and in the browser As Twitter offers clients of the API either response both data formats were tested with a sample tweet entity to establish which would be faster to stream faster to parse by browsers and easier to parse cross browser see Table 4 2 Response Size Time to Parse ms Cross Browser Parsing Method Bytes XML 2534 1 38ms Varies ActiveX DOMParser JSON 1715 0 88ms JavaScript eval function Table 4 2 JSON vs XML Attribute Comparison Based on these results the parse time of each format was quite similar in Google Chrome the browser used for testing However JSON s smaller footprint and the ability of browsers to natively parse the language meant that it was the logical format to use in Pulse One interesting caveat of this decision was the resulting differences observed between Google Web Toolkit hosted mode and deployed mode Hosted mode in GWT does not fully translate client side code to JavaScript Instead it is run as standard Java bytecode on in the Java Virtual Machine Hence it is not until deployment that speed gains from using particular
200. rch pad E Es a en Oo a E 14 222 ends MIP sara ok dieses tee dns ly heads oh ahve a is eer Uae UE es 2 15 22 Bendik UC e 44 4 e Beak de eee A Oh ci ee da i 16 22 AV AI 16 22 3 Regumements Elicitation sewa h A E AAA 17 o OMCIUSIOM ete amp Grete he ek os Be eee ae e a Y 17 Analysis amp Architecture 18 3 1 Application Requirements 0 0 0 eee 18 Oe Ore TECHNOlO GIES AA Gh aes Se Se Ok whee I ae ee t 19 3 2 1 User Application Framework 19 322 1 ErameWwork SelecuOM 20 ye prada A ee d 22 Side DOLE N REMERA 22 2 24 Retrieval Plattorin SeleCHON as gigs a de wah E a a e i 24 E A a AA 24 Sid GONCIISION ea e e Se GE a e a RR T 25 Data Processing 26 4 1 Data Processing Requirements 2 2 00 eee eee ee eee 26 42 pplication Design ow a ds 4 oe ace poke aoe Be we we Pe oe Be a ee 27 AZ ATCMIO CUE Sete eS BEES Cok OSs SY ees RES 28 ALL TOPADO we or sich oe Meta ve ho ae E ele A A ke a Ae ee hae Y 29 A sata SOUICe e oo ee do Ee dba de do 29 Bee Wee EAS e dera e GG SE Sk Wena es tee ae Ce Se Be SE a T 31 4A Dala Formak 21 42 8554 a O A SE ee we 31 Ae INE SESTUCIE e a ds ada e ad e a a a 31 A A A o W a Meee Baw A SS a 32 Ay DICAMIOS 2436 oo bo ek See me dd Baek e eS 33 AS AP COMECUI NO be O EIA ak ES 33 4 5 2 Connection Implementation e eee 34 4 5 3 Writing Streamed Tweets 2 0 0 0 eee ee ee ee 35 AS treats ISSUES e n a a ee
201. reduces the accuracy of sentiment analysis 3 Whether or not to use the POS tagger The POS or part of speech tagger splits each tweet s text into adjectives verbs nouns and adverbs These are then analysed for sentiment according not only to their content but the part of speech that they belong to However this takes a considerable amount of time You can switch this off to greatly speed up sentiment analysis however analysis will then use a combination of all entries in the sentiment analysis dictionary to determine a per word score rather than factoring in part of speech information 4 of Results to Plot on Map Set the percentage of all results to be plotted in the Map component Plotting itself takes little time but the process of geocoding each tweet that is establishing the location of the tweet from tweet metadata takes time With very large result sets this means the geocode process will be on going even once the initial query has been completed Also maps with a large amount of markers tend to use more system resources so for slower machines it is advisable to reduce this Note for all options that use percentages the number returned may vary slightly from the percentage specified This is because a random sample of that percentage is taken However for anything other than a trivial number of results this will have little effect Hashtag Query Results Once the hashtag query has completed either the results or a
202. rent query cache is needed by a component unsafe access is also available This is primarily used by components which operate asynchronously with results arriving after an update cycle has completed The QueryCacheManager consists of a list of QueryCaches each an individual cache of data per tinent to a single query and control methods for accessing specific cache entries Components can send data to the cache manager via the component manager Temporal range queries are not cached by the query manager rather the aggregate statistic files used by time queries are preloaded on the server side resulting in very quick response times 5 6 7 Navigation Modification Once a query has been accepted by the component manager it must notify the Navigation Bar so that it can be updated This happens in two places e Query History the appropriate history panel must be updated to include the new query either Date Query History for time queries or Tag Query History for hashtag queries e Compare Tab tag queries only a new entry in the Compare Tags list 1s required through which the tag can be selected for use in cached comparison queries The Navigation Bar is responsible for making these changes see Section 5 5 4 for further details 5 6 8 Update Mechanism The process followed to update components in Pulse varies based on the request type and whether the update is from cache or not The component manager deals with t
203. required data 4 Once the results have appeared information about that day s Twitter output should appear 5 The user guide details the information available using time search however feel free to explore each component to understand its functionality Scenarios These tasks require you to obtain certain pieces of information using the various components and features of Pulse There are multiple ways to find the answers each using different features of the system If you cannot find a particular piece of information please note this and continue to the next scenario Once you have the required information please note it in the Notes sheet Scenario 1 Which country tweeted using the tag cnn the most on February 16 Scenario 2 What time of day was the hashtag arsenal most popular at on March 2 Scenario 3 How many more tweets were tweeted featuring the hashtag ipad2 on March 2 compared to February 238 Scenario 4 What hashtags did the terms google and microsoft have in common on March 1 Scenario 5 What hashtag spiked in number of occurrences on February 14 Scenario 6 When in February did the hashtag egypt reach its highest number of occurrences Scenario 7 Where did the most popular retweet of February 9 originate Scenario 8 When did the hashtag winning start showing as a top tag Hint use the Options to set number of additional days to include in t
204. returns a valid string representation of that data A caveat of this approach is that client side components must know the string structure used so that they can deconstruct the response Though the structure is fairly simple it would be more efficient for the component manager to parse temporal responses into a uniform format usable by all components rather than delegating responsibility for parsing to individual components This would be addressed in a future version of Pulse Each parsing method will eventually return a HashMapjString String object The hashmap keys are Unix timestamps and the values are string representations of daily statistics for the specified data type Once all required days have been parsed the accumlated data hashmaps are encapsulated as an instance of the serializable nitialTemporalResponse class This object is then returned to Twitter QueryServicelmpl for transmission 119 6 4 4 Performance Analysis As discussed in Section 6 4 1 the most important attribute of the TweetFetch service is responsive ness Performance benchmarks were carried out using the application to establish response time for a range of queries Benchmarks were carried out under the following conditions e Network The client and server were run on separate machines using different network con nections to measure network latency e Client Google Chrome 12 running with 2Gb of available RAM on a single core Windows PC e Server
205. reverse property The initial approach then was to simply specify the tweet UID as a reverse meta key so that rather than having to perform any queries each tweet could be looked up knowing only its UID However enabling reverse key lookup caused daily merging to effectively halt described in Section 4 6 5 A fix was never found to remedy this problem so reverse keys were not utilised by the final appli cation Once the tweet strings have been returned to the TagQueryManager they are checked for integrity This is a workaround to a bug described in Section 4 6 4 Any tweets obtained from an index created whilst the bug was still in place are checked to ensure they are under 5000 characters long Those that aren t and hence have likely been truncated are excluded from the final result set The final TagQueryManager task is to construct a JSON array of retrieved tweet entities Testing showed that transfer of a single serialised string is considerably faster than transfer of array of 116 Return array of JSON strings 1 per tweet serialised tweet objects or even an array of strings As JSON is plain text the tweet array is easily represented in string form Full testing of each return type tried is described in Section 6 4 4 The JSON string is returned to TwitterQueryServiceImpl which in turn creates an nitialSingleRe sponse InitialSingleResponse is a serialisable class used by hashtag queries to encapsulate the JSON string
206. ried by the client application for tweet data The following section details the approach taken in completing these mergers 40 Load hourky tweet files into collection set currentFile to 0 Read currant file Construct new document from next tweet using Send document to Does current file have any more tweets qe custom document class Terrier indexar based on indexing mode No EOF exception thrown Collection is empty All s the current file the last tweet file Sel currentFile to documents indexed currentFila 1 Figure 4 8 TweetCollection Logic Merging Approach Pulse uses the mergeTwolndices method provided by Terrier s Indexer class to merge indices in a pairwise fashion During the first round of mergers the indices to be merged are loaded from the hourly indices folder These are merged and output as 12 2 hourly indices within a new folder dailyIndices From this point onwards all mergers take place within the dailyIndices folder As the pairing off of indices progresses eventually only three indices are left Pulse merges two of these then merges the resultant index with the leftover index This merger tree structure is shown in Figure 4 9 Interim indices are deleted at the end of each complete daily merge The merging process is conducted three times for each day folder once for each of the three index types 755 Created amp stored in 555 Created amp stored
207. rm of sentiment analysis 2 3 Conclusion In this chapter Twitter was introduced as a social networking platform with its own distinct termi nology communication paradigms and constraints A series of tools which can be used to analyse output from Twitter were discussed highlighting particularly useful features and identifying appli cation weaknesses These discoveries will be used to help form requirements for the system In the next chapter the problem domain is analysed and a design for Pulse is proposed 17 Chapter 3 Analysis amp Architecture Having reviewed available Twitter analysis tools the next stage in the project development process took place problem analysis and system design In this chapter we discuss the high level re quirements of the project introduce the two major subsystems describe the overall architecture of the toolkit and evaluate numerous technology options available for each subsystem 3 1 Application Requirements Requirements for the application were elicited through discussion with the project supervisors and by analysis of existing tools performed in Chapter 2 Each requirement was categorised using the MoSCoW prioritisation technique as proposed by Clegg 47 The necessity of each requirement was decided by considering its impact on achiev ing the system aims Those vital for success became must haves those which are necessary but need not be fully implemented became should haves
208. roadcast another user s tweets if they like them called retweeting Retweets indicate some of the most popular content on Twitter as selected by users so Pulse tracks the most retweeted tweets each day Quick Start Getting started is simple e Take a look at the Pulse Tips section for the most useful starting information e Give Pulse a query and click Go Mar 06 2011 Successful hashtag queries e g glasgow will result in the following Location Component Sentiment Analysis Component Mi Tweets ERRASSE Pee Ske SEE EEE Tagged With Component Geographical Tag Volume Heatmap In Context Component Component Component e Tagged With Component view tags that were used in the same tweet as the query tag and in what volume e Location Component see where tweets came from and what people across the globe are saying e Sentiment Analysis Component visualises the opinions of people towards a given hashtag with samples and statistics e Tag Usage Volume Component see how often a tag is used across the day e Geographical Heatmap Component find out which countries most tweeted a tag e In Context Component additional information about the query and the day s tweets Successful slashtag queries e g glasgow edinburgh will display the following components Location Duo Component Tag Volume Duo Component Stelle AAAA ARE ALOE OES b f
209. ry 1 Sun Mar 06 2011 2 Tue Mar 15 2011 3 Fri Feb 11 2011 Compare Tags Panel Once you ve queried two tags regardless of the type of query you can compare their results through special Duo components This can be achieved through the Compare Tags panel Compare lags Compare 2 queries Select 2 queries below to compare their results Tag 2 Selection List Tag 1 Selection List Select one Waiting for first selection Compare Compare Button 10 Compare lags Compare 2 queries Select 2 queries below to compare their results Options Panel The Compare Tags panel has 2 selection lists one for each tag to be compared You cannot compare the same query twice though you can compare the same tag in 2 different queries see left Additionally 2 queries need to have been made before you can compare The Compare button Compare will remain disabled upper right until the requirements for comparison have been met at Compare which point will it be enabled lower right Using the Compare Tags option is analogous to a slashtag search except all of the results are cached already see the Pulse Tips or Querying Pulse sections for more information on slashtag searches The Options Panel is used to set parameters for queries made within Pulse Options Because these options apply to specific parts of the system they are explained in Hashtag Queries Retrieved Results 1
210. s and total users followed to provide a simplistic assessment of whether this applies to individual topics as well as across the Twittersphere In addition it filters each tweet to establish certain characteristics occurring across a result set For example are users asking questions or making statements when using a certain tag Are most posts retweets or original content How often are popular users tweeting about a topic It was also used as a testbed for developing component prototypes Creating a new component takes time so to enable quick feasibility testing of proposed functionality tabs can simply be added to this component showcasing new features Any data processing or cache requirements can be added as required and should the features be deemed worthy they can later be moved into a new component Approach In the tweet categorisation view each tweet s content is scanned to check whether any of its content matches certain filters These are Questions if the tweet text contains any question word who what where why when or a question mark Mentions if the tweet text contains lt username gt e Retweets if the tweet text contains RT Popular Users if the tweeting user has gt 5000 followers Unique Users how many tweets in the result set were from unique users 89 e Facebook Tweets how many tweets originated from Facebook The results are then displayed as absolute totals and as a percen
211. s was most popular These can be switched using a drop down list in the title bar of the component As with the Location Component the timezone lookup process may return results for only one or neither result set both scenarios should be handled gracefully The test cases in Table 5 14 confirm this is checked by the component Test Case ID ETE Both result sets have 1 or more countries identified from tweet timezones A Only one result sets has 1 or more countries identified from tweet timezones Neither result set has any countries identified Table 5 14 Test Cases Geographical Heatmap Duo Component These were implemented by the tests in Table 5 15 Slashtag query where both Geographical heatmaps Success geo heatmap result sets have 1 or more with tweeting countries displayed eae map countries identified highlighted comparison generated map generated Slashtag query where only Geographical heatmap Sucess geo heatmap dis l result set has 1 or more with tweeting countries played countries identified highlighted no comparison map Slashtag query where nei Error message within com Success component dis ther result set has any coun ponent plays error message tries identified Table 5 15 Tests Location Duo Component Future Development Whilst the Geographical Heatmap Duo Component comparison map is useful the single hashtag map provides little functionality over and above the Location Com
212. s within each statement the wording inclining users to el ther agree or at worst not say either way However the large volume of qualitative results from the desirability analysis meant weak agreement or nonchalance could be compared to words selected and comments made by the same participant Together it was possible to infer improvements and opinions much more so than by analysing the quantative usability data alone 7 5 Conclusion The user evaluation described in this chapter aimed to assess the usability desirability and useful ness of Pulse with end users An empirical evaluation method was defined in order to establish baseline participant knowledge teach participants how to use the system let participants explore the system and finally understand participant reactions to the system The results show that Pulse has achieved each of these aims In both parts of the post evaluation review participants expressed positive sentiment towards the functionality usability and desirability of Pulse 71 of all qualitative feedback was praise for the features and design of the system as well as over 92 of all quantative usability feedback It also confirmed that Pulse was of interest to users and that it has something new to offer over and above existing tools Furthermore qualitative evaluation techniques showed that participants not only found the system usable but also engaging fun and entertaining to use 141 Perhaps most cruc
213. se are content units which perform manipulation and presentation of data re trieved from a query Each component provides a unique view on the retrieved Twitter data oc casionally utilising services provided by server side application During execution components are generated depending on the query type made hashtag slashtag comparison or temporal range with each query type having a particular set of components utilising the returned information 5 7 1 Component Specification Each component is derived from the NewComponent abstract class This class specifies basic func tionality every component must have and provides functionality common to all components It also defines the size and layout of each component Concrete implementations must extend this class and define component specific behaviour Component Structure Each component within Pulse has the same generic structure shown in Figure 5 17 This structure is defined by NewComponent which automatically configures the appropriate height and width given the size of the screen as well as performing the required GWT initialisation and applying the appropriate CSS styles Components should display a name for the component as well as providing any additional toolbars that the component requires inside the title bar The widget area is space provided for presenting the results of that component s data manipulation Functionality The NewComponent class defines functionality c
214. service e sentiment pos tagger file path to the sentiment analysis POS tagger file Comments can be added to the file by prefixing a new line with the character Practical instructions for deploying Pulse were consolidated into an Administrator Guide This has been included as Appendix E 6 7 Conclusion This chapter described services server side applications that provide support to client side com ponents The main tweet retrieval service Tweet Fetch was described in detail explaining the development process and discussing the implementation details of the final system Performance analysis was also carried out to establish system responsiveness Though certain queries take longer than the 10 second target to complete the majority of those tested did return within the time limit For those that did take longer several areas for improvement were identified that could be developed in a future version The next chapter introduces an empirical evaluation carried out with users to establish the usability and desirability of Pulse 123 Chapter 7 User Evaluation Ensuring that Pulse delivered a positive user experience was one of the key system criteria defined at the start of the project The user evaluation described in this chapter provides an empirical process to establish how successful Pulse is in meeting this criterion through both qualitative and quantitative means 7 1 Evaluation Aims The user evaluation prov
215. single line with the X axis fixed to represent time The Y axis and color attribute can be change to represent other variables Selecting a point within any line will show details for the tag represented by that line at the given point in time It also highlights that line within the graph area Line view is shown below cropped for space E ff 51 632 11 02 2011 View Functionality The best way to discover the capability of the Trends Component is to use it there are lots of ways to pivot daily data However the following table gives an overview of the main functionalities available 28 e Action Scatter View Bar View Line View Hover over chart Highlight specific node bar line show data for each node bar line visualisation method axises colour shape These functions apply also to selecting a tag from the tag list Selects specific node bar line in Additionally Selected Tags list fades non selected zooms in on tag nodes bars lines out and adjusts axises Click chart node bar line Show trails as Click Trails node moves over time Click and drag to Option to zoom in on smaller area of draw box in graph for closer detail of close chart area together nodes bars Trend Component Options The majority of the options described in the Time Query Options apply to this component e g number of tags upper lower boundaries whitelists etc Place Device Component
216. ssible from the site This limitation restricts the amount of trend tracking that can be performed to only the very recent past which for some queries is not sufficient 14 Euwjitter Qy 100factsaboutme x Search Advanced Search Results for 100factsaboutme 0 05 seconds Feed for this query ee 100factsaboutme 1 I m to lazy to write them ALL A i 3 days ago via web Reply View Tweet i il Any Language e 3000 recent retweets Ciaissiesir 100factsaboutme Winning x 100 Translate to English about 7 hours ago via HoofSuite Reply View Tweet 500 recent retweets Trending topics AA AA Figure 2 2 Twitter Search Furthermore the Twitter Search application only provides a single raw view of Twitter activity Though searches can be made more complex by using boolean operators or emoticons they still only return a list of tweets No information is given on how ranking is performed so the results themselves may be subject to internal Twitter filtering 2 2 2 TrendsMap TrendsMap 5 shown in Figure 2 4 is a geographically focused tool which shows trending tags in any location across the world Tags prevalent in particular locations are plotted on the map by checking the user s location Each tag is then sized relative to its popularity Clicking on any tag generates a real time update stream of tweets featuring the tag A democracy A EA rickmerr e Huen E casto ag Na Hotta A canu
217. sssesesssssssssssscsesesesesesssssscsesesesescsessesssssesesesesesessseseseseseseseseseeesesesees 32 A 33 What is Pulse Pulse is a social media analysis toolkit More specifically it allows for detailed analysis of the Twitter domain about any topic you might wish to investigate across multiple time periods in comparison to other topics You can compare opinions on a topic follow tweets across the globe find out people s attitudes to world Pulse a social news see which of a set of products is more popular observe Twitter usage volume over time and more media analysis To quickly get started using Pulse see the Quick Start ki section To learn more about Twitter see What ts tool it Twitter What is Twitter Twitter is a microblogging service that asks it s users what s happening Users have 140 characters in which to post status updates about anything they want opinions on the world around them conversations with other users what was on TV last night Within each tweet users can specify hashtags Hashtags are simply any word in a tweet prefixed with a This instantly becomes a form of inline categorisation and you can search for tweets featuring any hashtag within Pulse just prefix your query with Users can also mention within a tweet This allows users to reference another user inside their tweet by using their username and the symbol Finally users can b
218. st below based on how resource intensive it was e Inline The task is not resource intensive and can be performed within the main streaming thread e Concurrent The task is resource intensive but can safely be delegated to another application thread without affecting the main streaming thread e Offline The task is very resource intensive and may cause the application to lag It should be performed in a separate application Table 4 1 shows the final categories for the key tasks to be performed by the application Task Typical Time to Required Resources Category Complete Parsing library Table 4 1 Task Categorisation Having categorised each task a high level architecture diagram was constructed which described in broad terms the general application structure This is shown in Figure 4 3 The Streaming thread controls application startup and initialisation as well as directory creation and tweet streaming itself When appropriate new threads are created to manage the period writing indexing or cleaning of tweet data This is all stored within a central data repository containing minutely hourly and daily data files This datastore can be accessed by the client application for further manipulation and presentation of the results Finally the statistic analysis required is 28 Streaming Main Thread Writer Periodic Runnable Statistical Analysis E Data Store Separate Application Index Manager Periodic
219. t such as user data however it also increases the amount of storage required for every Tweet object Though this 32 is certainly a non negligible storage increase a single tweet entity 1s often 5000 bytes or more it provides future component developers with a full and complete record of each tweet Given that Twitter only offers access to a very recent history of tweets on any of its non commercial streams this was decided as vital both for future development and also before the full suite of components to be provided in this release of Pulse was decided upon 4 5 Streaming One of the main requirements of the data processing application was to stream tweets from Twitter into manageable logical data structures for later manipulation The streaming process implemented was integral to completing this requirement and encapsulates the end to end process of connection to storage on disk 4 5 1 API Connection Connection Considerations Pulse connects to the Twitter Streaming API using an authenticated long lived HTTP connection Provided a valid username and password is supplied in the case of Pulse my own Twitter username and password with Gardenhose access granted tweets will continue to stream until e Twitter performs network maintenance in this case the application will attempt to reconnect after a linearly increasing sleep time e The connection is closed application side in this case the client will attempt reconnect after
220. t Cases Location Duo Component These tests are important geocoding is a process separate to retrieval of results so 1t may be that only one or neither result set has any markers to show The Location Duo Component needs to handle each of these situations Checks for these conditions were implemented by the tests in Table 5 9 ID Slashtag query where both result sets have 1 or more geocoded tweets Slashtag query where one result set has 1 or more geocoded tweets Slashtag query where nei ther result set has any Map with differently coloured markers for each result set clickable markers to toggle visibility of result sets Map with one set of mark ers no markers in title bar Empty map nothing in title bar Success map shown with markers toggles included in title bar Partial map shows relevant markers but title bar mark ers still appear Partial map has no mark ers but title bar markers geocoded tweets still appear Table 5 9 Tests Location Duo Component These tests highlight one of the difficulties of handling asynchronous requests As the component doesn t know whether any tweet can be geocoded until it tries markers are added at the start of the update to the title bar If all geocodes fail or the plotting option is set too low no markers will appear on the map but the title bar markers are not removed Whilst only a visual error it suggests the map has markers when it does not poten
221. t data so a secondary management class TerrierSearch was created to control Terrier querying and index manipulation The lowest architectural level is the Pulse data store All tweet data is stored here after processing by the application described in Chapter 4 Once all processing is complete managers pass a finalised response to TwitterQueryServicelmpl for transmission back to the client Each of the stages plus the data sent interchanged between each stage is described in Section 6 4 3 6 4 3 Process Figure 6 2 gives a high level overview of the process followed once a request has been received Send query to correct query Process request manager Establish request Send response to initialised type client Create new specific query properties file manager instances Figure 6 2 Tweet Fetch Service Process Initialisation amp Request Delegation On first invocation the Pulse properties file is read to locate the system data directory Once com plete new instances of the TagQueryManager and TemporalQueryManager classes are created 113 The TwitterQueryServicelmpl class then informs each manager of the data directory location As the Tweet Fetch service is persistent this process happens only once Each request sent to the service has an explicit request type detailed in Section 5 6 4 Twitter QueryServicelmpl uses this to delegate responsibility for the query to the correct manager class Ta
222. t manager is used to geocode translate textual location data into a specific latitude longitude pair each tweet based on whatever location information is supplied 78 Test Case ID Slashtag query results are returned for both tags 1 of 2 slashtag query tag results have no associated hashtags 2 Both slashtag query tag results have no associated hashtags Duo cached query is selected Table 5 3 Test Cases Tagged With Duo Component ID 1 A slashtag query with re Split pane with 2 sets of Success component dis 2 3 sults for both tags a results displayed query plays split pane and 2 tag at top of component clouds volume in brackets amp tag cloud Slashtag query only 1 tag Tag with results as test Success component split has associated hashtags case 1 Tag without query pane tag with cloud tag tag at top of component without volume in brackets Slashtag query neither tag Output from test case 2 Success component dis has associated hashtags plays query tags and vol umes 4 Duo cached query from the Quick display of output Success data loaded from Compare navigation tab from test case 1 3 cache and displayed Table 5 4 Tests Tagged With Duo Component Purpose The component has two main purposes The first 1s to 1llustrate where exactly tweets for a particular topic are most popular in the world indicated by large clusters of markers over locations with heavy Twitter a
223. t text as well as meta information and links to external content Having started operation as twttr in 2006 1 it has since grown to be ranked 10th worldwide in the Alexa Traffic Rankings 10 of most visited websites Whilst Twitter has not disclosed the current number of users utilising the service in April 2010 1t was announced that there were 105 000 000 registered users 8 2 1 1 Tweets 2 Username Hashtag codinghands lolrme wa be just started working on pulse maybe be of interest to EvelynG 1 Favor Reply Delete Mention Figure 2 1 A typical tweet taken from a user timeline Tweets are the primary communication medium within Twitter Consisting of a 140 character mes sage each tweet is listed on the tweeting user s profile page as well as in the stream of each of that user s followers The technical definition of a tweet as indexed and used by Pulse components is described in Section 4 4 12 2 1 2 Social Relationships Unlike most social media outlets Twitter has two main relationship structures that allow users to share tweets Rather than providing a reciprocal friend mechanism that validates both parties as equals users can be followers of and or followed by any another user As long as a user s tweets are public that user can be followed by anyone However the user being followed is under no requirement to follow the requesting user in return Following another user automatically adds
224. tage of all tweets In the follower following view each tweeting user is plotted on a scatter graph using the user s followers and users followed counts The component utilises a TabPanel to enable switching between multiple content panels Com ponents that feature multiple data views each needing to be distinct can use TabPanels to show only one view at a time The remaining views are listed horizontally as clickable tabs hidden until required Data Requirements The component requires the text of each tweet in the result set as well as the follower following totals of users responsible for each tweet The query cache used by this component stores a comma separated string of follower following total pairs as well as a TweetSetStats object This holds the total Component Testing The In Context Component has no secondary filtering e g geocoding or timezone lookup so will always show data as long as there are results Table 5 16 shows the test cases defined for the Tag Volume Component fd 1 Hashtag query results are returned query Hashtag query results are reed are returned Slashtag query is converted to hashtag query due to 1 query tag having no results od Cached query is selected Table 5 16 Test Cases In Context Component Table 5 17 shows the tests derived from each test case and the results of running each test Duo Equivalent The Duo equivalent of this component adds another tab for a sec
225. te functionality but 1t would be difficult to extend it to multi query comparisons or for different types of query Given additional development time this would be restructured further centralising the data required by each component and reducing the number of transformations between request types 5 6 9 Tweet Parsing Tweets are parsed client side by the ClientTweetParser This class utilises JSNI methods to quickly parse the JSON results array into more manageable Tweet objects later used to update each com ponent The server side tweet fetch service returns results as a string representation of a JSON array with each element a single tweet entity The ClientTweetParser uses the JS JSON parse function to convert the string into a JavaScriptObject in GWT a direct representation of a native JS object The required data is then extracted using native JS methods after which the newly created Tweet object is added to the parsed list T2 When parsing is complete the tweets are stored within the component manager for later use by individual components 5 6 10 Component Area The component area consists of all the space available within the user interface not taken by the Query or Navigation Bars On creation components are added to the component shell which fills the entire component area Addition and removal of components in the component shell is also handled by the component manager 5 7 Layer 3 Components Components in Pul
226. ted as a black box component for purposes of testing on the assumption that provided with valid data and appropriately configured parameters it would perform the function expected of it Table 4 11 shows the actual tests performed upon the system based on the test cases with the test results The failure of test 3 resulted in a major design change Having all 3 indexers run simultaneously would have considerably increased the indexing time however each requires access to the same tweet files throwing an exception Given that the hourly indexing process still completed in under 40 minutes when run sequentially it was deemed unnecessary to further investigate a possible solution opting to wait for each indexing thread to die before continuing Table 4 12 describes the test cases relevant to the daily merging component of the application As discussed in Section 4 6 5 merging proved too troublesome to include as a component within the indexing pipeline The results of testing index merging within this pipeline are shown in Table 4 13 Some peculiar bugs relating to implementation details of the daily index process were discovered during testing In test case 2 an odd number of initial candidate indices would result in an error This is because the first round of mergers from 24 to 12 usually places the interim indices in a new directory dailyIndices During the second round of merging all candidate indices are found in the dailyInd
227. ter EXPerienos 24 00d Era a A e dd 132 Roe CONCHISIONS gt ue dora e a ds de ee e A Be ke BS 133 7 4 Post Evaluation Results Analysis 2 2 2 2 eee ee ee 134 Dies ADCS aD IY eeg ica a fh eres o Eee as eh is ean ase JE ee amp 134 ee AOSADMIVY o dr e Bie hh aa race a aS E r E Be es 138 Tey COMCIUGIONS 2 e ayer a Bo Se ai a Ee ee ee a Se a 141 Conclusion 143 SF SUMMA Las E A A A ee ee d 143 8 2 P tute Development errai e a A o A BE 144 User Guide 150 Pre Evaluation Questionnaire 184 Post Evaluation Review 192 Evaluation Task List 198 Administrator Guide 204 Chapter 1 Introduction This document describes Pulse a toolkit for streaming and analysing large volumes of data from Twitter a social networking website This chapter describes the problem definition motivations for solving such a problem and the aims of the system to be developed 1 1 Problem Definition The volume of data being created by Twitter users is incredibly large and access to archived data is limited Additionally few utilities exist that provide the ability to analyse Twitter posts en masse in great depth over multiple variables Being able to study this data mining it to understand trends and topics of note at a particular time would be useful in many commercial and academic fields Several systems already exist which provide some level of insight into tweet trends However as discussed in Section 2 2 most of these systems consi
228. than the potential benefit given the time available for development This also provided motivation to ensure the application was actually robust and correct rather than just being very good at recovering from failure 4 8 Adherence to API Usage Conditions Ensuring Pulse used the Twitter streaming API correctly was a requirement of Gardenhose access and as Pulse s core data provider adhering to these mandates was a critical requirement The re 43 quirements laid out by Twitter can be divided into two types e Technical Requirements mandates that specify how the application should interact with the API These include use of appropriate connection types handling of connection failures and dealing with non 200 HTTP codes Inadherence to these requirements would result in tem porary or permanent suspension of access to the API e Data Handling Requirements implementation details that must be considered when using the API These include tolerance of duplicate or out of order tweets data integrity checking and graceful parse failure handling Lack of consideration around these requirements would result in missing data corrupt data and unexpected application failures The technical requirements were dealt with by considering points at which the processing applica tion could stall and if they occurred establishing which party was responsible for that failure From there appropriate actions were defined that either rectified the failure or
229. that user s tweets to your timeline The timeline is the default page displayed once login to the Twitter site is complete It lists the most recent tweets of a user integrated with the most recent tweets of all that user follows As new tweets are posted this list 1s automatically updated Similarly being followed by a user means that your tweets are displayed in their timeline 2 1 3 Tweet Terminology amp Usage Hashtags Though a tweet can only contain text users can mark particular words as hashtags Each hashtag is prefixed by a hash symbol as shown in Figure 2 1 Hashtags allow users to categorise their tweets and signify to other users that tweet s content Rather than a static hashtag list existing a hashtag is implicitly created after its first use within a tweet Futhermore a tweet can contain as many hashtags as allowed within the character limit Twitter uses hashtag information as one variable in determining trends that is the most popular topics on Twitter Mentions In addition to hashtags tweets can also contain mentions These are explicit references to an other user within the tweet References can be identified by the symbol prefixing a user name Mentions are another way of adding meta information to a tweet signifying that the tweet in some way concerns the user being mentioned The user being mentioned will see this tweet under the Mentions tab of their profile Any user can be mentioned in any
230. the two tags specified indicating differences and similarities in usage between each The results of slashtag queries are displayed using the following components each a modified version of its hashtag query counterpart e Tagged With Duo Component shows both query tags and their respective volumes as well as all co occurring tags In addition highlights co occurring tags common to both query tags and in what volume 57 e Location Duo Component marks each tweet from the two result sets with markers on a world map a different colour for each query tag e Sentiment Analysis Duo Component provides sentiment analysis of tweets featuring the query tags as well as sentiment comparison e Tag Volume Component shows the distribution of each tag s usage over the course of the date selected e Geographical Heatmap Component shows the total number of tweets per country featuring the query tags as two maps as well as a comparison map showing which tag was most popular in each country e In Context Component provides additional contextual information for both query tags 5 4 3 Temporal Range Queries Temporal range queries in Pulse provide an overview of Twitter activity over a range of days including top tags most retweeted tweets and the most popular links Users can select a range of days and compare differences over time amongst other configurable options Temporal range queries can be issued by leaving the search box empty a
231. tially leading to user confusion Future Extensions The GWT wrapper of the Google Maps API provides only the official maps functionality The native JavaScript library has unofficial extensions which allow for more advanced map overlays such as clustering of markers over locations and customisable information windows These could be includes via JSNI to add additional functionality to the marker popup windows such as user information or highlight areas with intense Twitter activity 83 5 7 5 Tag Volume Component The Tag Volume component shown in Figure 5 21 shows how tweet usage for a term was spread across the query date I E 10 il EP EEL LL EE EFL L LL EL EE EE ES Mave iy ee ee KA oF RS RA ARS e e ae Ro ne CESSES a E amp So nag as SSE EE fs e Figure 5 21 Tag Volume Component Purpose The functionality provided by this component is fairly rudimentary a graph detailing how tweet usage varied hour by hour over the course of the query date Envisioned uses include detecting how long after a breaking new story or event that Twitter chatter began or judging when users of a particular hashtag are most likely to be active on Twitter Data Requirements This component requires the publication date of each tweet in the result set The query cache used by this component stores a list of period names for the X axis legend and an array of numerical tweet volumes Data Preparation amp Visualisation The total
232. timent This could then be used to inform decision making will I buy X or Y or illustrate feeling towards a topic I hate love X 16 Twitter Sentiment Type in a word and we ll highlight the good and the bad xbox360 Search Save this search Sentiment analysis for xbox360 Sentiment by Percent Sentiment by Count MA E Figure 2 5 Twitter Sentiment 2 2 5 Requirements Elicitation From the tools reviewed it is clear that most consider only Twitter data from the recent past to be relevant Almost all of the services reviewed use either live results instantaneously requested from Twitter or a history of at most one month This is potentially an area where Pulse could specialise providing much longer term trend tracking and analysis especially for terms which are almost always trending certain users brand names companies etc Most of these tools are also only focused on one particular aspect of the Twitter domain Pulse should provide a more broad comprehensive overview when queried by presenting data to the user in different ways with each data view highlighting a particular facet of the result set However developing multiple visualisations should not be at the cost of accuracy or reliability each individual data view Sentiment analysis tools were also reviewed and based on the satisfying and informative user ex perience had with the Twitter Sentiment tool Pulse should ideally implement some basic fo
233. tive Positive Positive Negative Negative Negative msnbc Buy up Mars How does 160 billion sound NASA scientists and their colleagues Volume 14 12 15 14 5 ar http on msnbc com glWroE tech science L JL Percent 11 7 12 77 15 96 14 89 5 32 Another excellent edition this week on Oregon Field Guide bats native turtles LIDAR http bit ly erqnOl science Oregon eco maps I Very Positive Moderately Positive a i Sciennce Daily Metaknowledge Powerful new ways to electronically mine research The E Slightly Positive Internet has beco http bit ly nf47VS science E Slightly Negative Metaknowledge Powerful new ways to electronically mine research http bit ly fm07Ba KM Moderately Negative science Wi Very Negative New amp biology blood circulation iPhone edapp http bit ly bioblood iTunes http bit ly bFujYd anatomy science bio health cells Moderately Positive RT BenCOGrimm Declaring your love the scientific way on Valentine s Day http fyfrog com h2fiyplj science atheism Figure 5 24 Sentiment Analysis Component Motivation Analysis of tweent sentiment was identified during the existing tools evaluation in Section 2 as a desirable feature for the Pulse user application Understanding tweet sentiment has a wide variety of applications These include searching results to inform decision making discovery of new interests and opini
234. tive questions such as which is more talked about in the UK labour or conservative The only existing tool that was found featuring similar functionality was Tweet Volume 6 however this has since been disabled for public use Data Requirements The component requires the timezone field of each tweet in the result set The query cache used by this component stores the calculated country occurrence pairs as a hashmap Timezone Lookup amp Visualisation As discussed previously real geocoding requires both external HTTP requests and time However tweets do provide an estimation of location via the tweet user timezone field To quickly approx 86 imate the volume of tweets from a particular country this component compares the timezone field against a pre defined list of common Twitter timezones and their respective countries Although this is not as accurate as geocoding it does occur instantaneously whereas geocoding 1000 results takes in the region of 100 seconds The country volume list is then used to construct a data table powering the map visualisation constructed by the Visualisation API Component Testing Table 5 12 shows the test cases defined for the Geographical Heatmap Component Test Case ID Hashtag query results are returned timezone lookup returns at least 1 match ed Hashtag query results are returned timezone lookup returns no matches a Slashtag query is converted to hashtag query due to 1 quer
235. tmap Component The Geographical Heatmap Component shown in Figure 5 22 enables users to quickly identify which countries most frequently tweeted a particular tag without waiting for geocoding The Duo version of this component also provides comparison functionality highlighting which tag was more popular in a country and by how much Purpose This component was designed to avoid the wait time associated with the Location Component Bal ancing processing time against result accuracy is a common challenge when updating components 85 Tweets 109 Figure 5 22 Geographical Heatmap Component and the Geographical Heatmap component aimed to alleviate the inability to geocode instanta neously whilst still giving a general overview of country level Twitter activity Countries are coloured on a sliding gradient scale proportional to the number of tweets attributed to that country Hovering over any country activates a popup panel detailing the country and number of tweets Motivation This component complements the Location component by aggregating tweet volume by country us ing a more appropriate visualisation Users can very quickly establish where in the world a tag was most popular and also in the Duo version of this component they can compare two tags popularity using the comparison heatmap Concrete questions that can be answered by this component include where did most tweets contaning wikileaks appear or compara
236. treaming ran as fast as possible The indexing manager 1s initialised containing each pipeline component it must execute Though all of the components know the source and destination of the data they require the indexing manager ensures that these are executed in the right order within their own threads Each pipeline component is extended from the abstract class PipelineComponent This itself is subclassed by two further classes IndexPipe and CleanerPipe The index manager runs all pipeline components subclassing CleanerPipe first then pipeline components subclassing IndexPipe each within its own thread The index manager waits for these processes to die before continuing with the next in the queue This prevents concurrent modification errors whilst reading tweet files though still allows the main tweet streaming thread to run independent of the indexing process This se quence is shown in Figure 4 7 The indexing manager also keeps track of successful and failed index processes enabling overall success of cleaning and indexing to be monitored and if necessary debugged This proved useful during the development stage of the application for establishing the root cause of problematic bugs e g the aforementioned concurrent modification file I O issue avoided by using thread joins 37 IndexingManager CleanerPipe new newt join new l l l Joint i newl l l l point i newl l l l l l i point
237. ts To Show 20 Link Display Limit Exclude web from Devices Exclude Web As Device Trend Tag Whitelist px Tag Whitelist Clear Tag Blacklist Trend Tag Blacklist E 23 24 Trend Tag Whitelist z Remove Selected Additional Days Information to Show the number of days either side the queried date to fetch results from For example selecting 5 then searching for 15 March 2011 will return results from 10 March 2011 to 20 March 2011 The maximum is 30 Daily Top Tag Limit Pulse Time Queries will display the top 50 tags for a day within the results by default Modify this setting to change this Tag Minimum Maximum Occurences Set boundaries for the number of occurences a tag must have to be displayed Setting a high lower limit will result in only the most popular tags appearing Similarly setting a low higher limit will show only the less popular tags Retweet Display Limit Set the number of retweets to display Generally only the top 20 retweets are stored for any given day Link Display Limit Set the number of links to display Device Display Limit Set the number of devices to show in the Places Devices Component A higher limit will show more devices however on low resolution monitors the line graph will quickly become crowded Default is 10 Exclude Web As Device Web is by far and away the most popular way to tweet on Twitter To see Place Devic
238. ts being anal ysed In this case the component cache still needs to be created to avoid errors when regenerating components from cache Table 5 19 shows the tests derived from each test case and the results of running each test 94 Test Case ID Hashtag query results are returned sentiment analysis 100 Hashtag query results are returned sentiment analysis 0 Hashtag query results are returned sentiment analysis gt 0 and lt 100 Slashtag query is converted to hashtag query due to 1 query tag having no results Hashtag query results are returned POS tagger is in use oe Cached query is selected with sentiment analysis results Cached query is selected with no sentiment analysis results Table 5 18 Test Cases Sentiment Analysis Component Test ID 7 o o o a I Input Hashtag query with results sentiment analysis op tion set to 100 Hashtag query with results sentiment analysis op tion set to 0 Hashtag query with results sentiment analysis op tion set between 1 and 99 Hashtag query with results Use POS tagger option se lected Slashtag query where only l tag returns results Cached hashtag query with sentiment analysis results Cached hashtag query with no sentiment analysis re sults Expected Output A tabbed component dis playing a pie chart sam ples and statistics Com ponent cache is added to query cache A message is displayed in side the component all oth
239. tuations participants retweet The majority of retweets are simply humourous comments that participants wish to share though based on the number of participants and their posting frequency it is hard to draw further conclusions PreQ T Q12 Update Types PreQ T Q13 Relative Time on Twitter 1 Most time Work related Personal In relation to news Twitter Figure 7 9 Update Type Comparison with Other Networks Finally Figure 7 9 a shows the type of update participants tend to post as well as the relative time compared to other social networks that participants spend on Twitter Figure 7 9 b As with update frequency participants tend to either spend a significant amount of social networking time on Twitter or very little The functionality provided by Twitter 1s quite different to the relationship based nature of alternatives such as Facebook so for those wishing to keep in touch with friends and family using social networking Twitter may not be the preferred option 7 3 4 Conclusions The pre evaluation questionnaire helped to unearth several properies of the participant group Most importantly Twitter is generally seen as a source of content rather than an outlet Participants primarily follow users of interest choosing to read those users tweets rather than post their own 133 It also established that social networking is ubiquitous amongst the evaluation participants Even participants with no deep technical experience ar
240. tweet regardless of the users relationship Retweeting Retweeting is the central mechanism within Twitter for allowing users to rebroadcast a tweet Retweeting a tweet results in a copy of tweet being posted by the retweeting user Tweets can be retweeted multiple times with particularly popular retweets being factored into Twitter s list of trending topics 13 2 2 Existing Analysis Systems A large variety of tools are now available which offer a particular insight into the Twitter domain A selection of these were reviewed to establish the capabilities of existing systems and also to identify functionality which is poorly represented Twitter analysis tools can be broadly divided into five categories e User tracking tools these tools provide individual users with statistics about their own ac count and Twitter usage Simplistic capabilities such as tweets posted and typical Twitter usage patterns are supplemented with more advanced features such as graphing of a user s connections network or recommendations of who to follow on Twitter based on the user s current network e Twitter specific interactions analysis many tools offer some view of how prevalent particular Twitter specific interactions are for example the most popular retweets being posted daily user rankings or commonly posted links e Self promotion tools certain tools provide specific functionality to aid users in attracting more followers Thes
241. uery Box a simple text box for entering hashtag queries Users can enter tags with or without a symbol Pulse will automatically assume tags missing the symbol are hashtag queries It also handles slashtag queries of the form lt tagl gt lt tag2 gt Pressing Enter whilst focus is on the search box will initiate querying 61 e Date Picker a text field which when clicked displays a calendar widget for selecting a date This field cannot be directly typed in a date must be selected by using the popup widget This provides a guarantee to the server side service that the date selected is valid when looking for the correct index to load e Search Button clicking this button will create a new request using the values in the search box and date picker and starts the query process It will also disabled the button until the query has completed 1 e all components have been updated Once the Go button has been clicked the Query Bar creates a new request to pass to the component manager The type of this request is decided by checking the combination of values within each input field if the search box has no value then the query is deemed to be for a temporal range If the search box has any text then regular expressions are used to check whether it is a hashtag or slashtag search Once the request type has been established options are validated and added to the request The option values are taken from the Navig
242. ugh the design of each component 5 1 2 System Requirements The functional requirements of the system developed naturally once user requirements had been established N B these requirements applies to the system as whole Each system layer and individual component has 1ts own specific requirements discussed in the appropriate section e Given a topic based query the system must generate a set of components containing relevant data and analysis of a single facet of the result set e Given multiple topic based queries the system must allow for comparison of each result set e Given a temporal range query the system must display trending content from the Twitter domain across the applicable time range highlighting changes and identifying patterns e The application must have some way of retrieving data from the Pulse data directory created by the data processing application 53 e Result sets should be cached once retrieved to allow quick regeneration of the appropriate components later in a Pulse session In addition to these absolute requirements the application should exhibit the following qualities e Portable as a browser application the system must be able to run across all popular browsers Fallbacks should be in place where a browser does not support certain functionality e Usable the client application must be easy to use and learn Functionality should be clearly labelled and help provided where necessary e Extensi
243. urly indices into a daily index in a reasonable time Terrier was configured not to use reverse key lookup for the meta index Reverse keys as opposed to forward keys are meta index keys whose 42 values can uniquely identify a document within a collection As tweet UIDs are stored as meta data within every index it would be useful to be able to lookup indices using this parameter however full 24 hour merging was never successfully completed whilst this property was set Whilst omitting reverse keys considerably improved the speed of daily indexing from an indeter minate amount of time to around 6 30 hours for all 3 mergers it meant that reverse meta data key lookups could not be performed This had an impact on performance during retrieval from the server described in Section 6 4 4 4 7 Failure Recovery Should the data processing application fail for any reason it was imperative that when restarted it could resume work starting from its failure point To this end the application performs checks on start up to establish whether it was previously streaming into the data directory or it has been tasked with a new streaming session At startup the application requires a directory path argument informing it where to create the Pulse data folder structure see Figure 4 4 Before streaming begins it checks within this folder to ascer tain if data from previous runs exists If not it will create new structures and begin streaming anew Howeve
244. users plus number of followers and users fol lowed The dailyStats log output contains a comprehensive overview of Twitter activity for each day How ever its file size is prohibitively large for real time use 30Mb per day and as a complete record of a day on Twitter contains data most users will find irrelevant e g tags which appear only once URLs which are posted rarely Figure 4 10 shows the data aggregation process Generate fae Parse Process N i Generate al e e dallyStatsTop10 lo weet fie tweet Tweet dallyStats_log g from sorted Yes YES hashmaps Store user info i ly Extract source fuserlD follower al gt sentiment add lo location URLs following count category count i add to hashmap E Add tag info Add retweet Info aio user to location user time hashmap Figure 4 10 Twitter Data Aggregation 45 Tags Tweet Vols Senti Cat RTs URLs dysa AM 24hows 6 AN AM AN AN All 6 i dailyStatsTop10 0 1 Table 4 7 Aggregate Twitter Data Comparison As Pulse temporal range queries aim to present an overview of the most popular Twitter topics of the day a subset of the above is stored within a different file dailyStatsTop10 log A comparison of data in each file is presented in Table 4 7 Both files store the volume of tweets per hour per sentiment category and per country these are simple arrays with negligible storage cost The largest omission from
245. ust stable reliable trustworthy and largely autonomous Where errors do occur they are appropriately logged and usually gracefully recovered from a fact confirmed by extensive and thorough testing The structures and indices created for holding data both on the file system and using Terrier allow for organised access to all the data required by client facing sections of Pulse These applications can rely upon the data processing application to provide them with tweets and statistics that are guaranteed to be correct and non corrupt The next chapter discusses how the data gained and stored by the processing application is used to provide a rich user experience on the client side 51 Chapter 5 Pulse User Application Client Side The client user application is the most important component within Pulse providing a way to query all of the data stored and interact with the results in a dynamic exploratory and interesting manner It supplies numerous views of the data retrieved and permits searching with multiple configurable parameters This section describes the approach taken in planning structuring and building the user application 5 1 Application Requirements The application requirements were split into two categories what the user must be able to do user requirements and what the system must be able to do to support the user system requirements Each of these is discussed further in this section 5 1 1 User Requireme
246. ve criteria 22 HBase HBase is a an open source distributed versioned column oriented store It is distributed in that it 1s built upon a distributed file system Apache Hadoop It is versioned in that each table is three dimensional where the third dimension is time HBase can be configured to store previous versions of a row for later retrieval Table 3 1 shows the analysis conducted for HBase Scalability HBase is built upon a distributed file system explicitly designed for very large data sets Scalability is one of the strongest reasons for selecting HBase as the application platform Retrieval Function The default HBase retrieval mechanism involves scanning each table ality row by row The scanning process can be filtered to exclude certain rows based on columns or column families but scanning large tables still takes a non negligible amount of time too long for use in real time A secondary layer then would need to either cache or index HBase results Retrieval Effi Scanning tables takes a disproportionately large amount of computa ciency tion to achieve fairy simplistic functionality Retrieval Effective Scanning each table is a laborious process not suited to real time ness retrieval A separate retrieval layer would be needed to implement real time data retrieval Required Infras HBase requires the Hadoop distributed filesystem to operate This in tructure turn requires a cluster of machines to a
247. very tweet Process tweets offline HBase ere pod tweets Winte tweels Twitter Streaming FEAR NGS rata bee iras to HBase API Figure 4 1 Initial processing logic of application Scan The difficulties in using HBase as a data store eventually became too numerous for it to be consid ered a stable option for the data processing application After deciding against HBase Terrier was adopted as both the storage and retrieval mechanism because of its l Speed very large indices can be created within effective time limits 2 Reliability given properly cleansed data Terrier is exceptionally reliable With this came large changes to the design of the application and the flow of data through the system This new logic is shown in Figure 4 2 The architecture of the application was then defined using this data flow diagram as a foundation 21 Every day Every hour Every minute Every tweet Establish Write tweets Merge hourly y Index tweets cp eens pelele Read tweet Parse tweet pea to file from from minute indices e o Twitter Streaming ll a aiii ENS le processed files sae AFI i list index Figure 4 2 Core processing logic of application 4 2 1 Architecture The application architecture needed to provide a rigid structure organising each processing task These tasks including streaming parsing writing indexing and analysing tweets To appropriately structure the application each task was categorised using the li
248. waited for an appropriate event or amount of time before normal service resumed These stallpoints are listed in Table 4 5 Stalin Resolution Strategy HTTP Status On making an HTTP connec Unable to con Sleep for 20s increase Code 200 tion to Twitter the server has re nect to Twitter time exponentially for sponded with a non OK HTTP each consecutive failure code Object read Null read suggests the connec Unable to Close existing connection from stream tion has been closed stream tweets and reconnect 1s null Table 4 5 Application Stallpoints amp Resolution Strategies Where the technical requirements above focused on correct use of the API the data handling re quirements were important for the functioning of Pulse as a client application Twitter makes few guarantees about the order and cleanliness of data it provides requiring clients to ensure they per form data cleansing on anything streamed Pulse mitigates these risks as described in Section 4 6 Resolution Strategy Malformed JSON object streamed is mal Tweet unusable Catch parsing exception or non tweet formed or unparseable as data discard tweet continue entity streaming Duplicate Tweet is a duplicate of another UID lookup Eradicate tweets from tweet tweet index for hour minute files before hourly will fail indexing using tweet cleaner described in detail in Section 4 6 Tweet out of Tweet is not in order with other Inaccurat
249. wing a map click the Retweet List button 31 Popular Links Component The Popular Links Component is a simple list of the top most tweeted links on Twitter for the query day O http is gd go4HT 1102 occurences ccurences http is qd UdxjBd 845 occurences As with the Retweet Paths Component the gradient for each Link Entry denotes the popularity of the link from solid red to solid blue For each link the link itself and the number of occurences is given Clicking on a link will open in a new browser window tab 32 Link Entry About Pulse was developed by Paul Holmes a level 4 student of Computing Science at the University of Glasgow The project was supervised by e Drladh Ounis iadh dcs gla ac uk e Dr Craig Macdonald craigm dcs gla ac uk Pulse 2010 2011 33 Appendix B Pre Evaluation Questionnaire 184 Pre Evaluation Questionnaire ETS Fixed minor errors 0 1 Initial draft Information Overview Before you attempt the task list we would like to establish your experience in the use of social media Twitter and analytic tools The following questions will ask about each of these fields and there are no right or wrong answers Please complete them as fully as possible Instructions This questionnaire has 4 types of questions e Yes No questions Explanatory questions where we ask you to explain and expand upon previous answers Closed rating questions
250. xample the Split Tag Cloud option list in the Tagged With Component Users did not expect these lists in comparison and slashtag queries as they did not appear when using single hashtag queries This meant they were often overlooked or ignored whilst searching for information Making this lists more prominent either through font size or colour would resolve this issue Statement 12 The user interface of Pulse is pleasant was included to assess user enjoyment of the system interface Every participant agreed with this statement describing it as bright clear vivid and professional Statement 13 liked using the interface of Pulse assesses how pleasant the UI was to interact with As with statement 12 users tended towards strong agreement median score 2 The application uses a number of interface widgets which all afford clicking dragging hovering and toggling each a simple but satisfying user interaction Statement 14 Overall I was satisfied with Pulse asks users to make a final decision on their opinion of Pulse It is gratifying to see participants display strong agreement with this statement and suggests success in achieving system usability In total 92 of responses 90 answers were of score 3 or higher indicating agreement with each statement As illustrated by the result graphs no response higher than 4 was given for any of the 14 statements This is likely the effect of positivity bia
251. y data source of Pulse the data processing application has ultimate responsibility for the quality volume and integrity of all tweet data and hence all results derived from this data From a functional perspective the main requirement of the data processing application was to stream tweets from Twitter and store the data within a format that could be later accessed and queried by the user application The process of loading querying and retrieving data also needed to be fast enough for real time usage Furthermore the format used must be organised in a way that naturally suits the various query types Whilst streaming tweets the application must stay synchronised with the rate at which data arrives and not build up a backlog As there are around 120 tweets per second to be processed the applica tion must perform all parsing and processing in an extremely small time window This influenced the choice of libraries used the methods in which data was handled and the level of concurrency utilised Certain queries also require access to aggregated statistics of a given day The application must be able to generate these statistics in some way and integrate the analysis results into the wider Pulse data directory From a non functional perspective the application must be able to recover from failures and handle errors appropriately The scope for unexpected errors is large so ensuring the application can gracefully deal with dropped connections Tw
252. y tag having no results 4 Cached hashtag query with geo heatmapresults Cached hashtag query with geo heatmap results 0 query with geo heatmap results s Cached hashtag query with no geo heatmap results fo Country is hovered over Table 5 12 Test Cases Geographical Heatmap Component Table 5 13 shows the tests derived from each test case and the results of running each test Hashtag query with results Geo heatmap with coun Success heatmap displays geocoding returns at least 1 tries highlighted by total with countries highlighted match tweets Hashtag query with results No results found error mes Success error message dis timezone lookup returns no sage played matches 3 Slashtag query where only Output from one of test Success output is one of l tag returns results cases 2 test cases 2 4 Cached hashtag query with Quick display of output Success heatmap displays so neamap cite temente without gt 5 Cached hashtag query with Quick display of output Success error message dis AAA pigeon Hover over country Info window above country Success info window dis with country name number of tweets nothing if coun try has no tweets Table 5 13 Tests Geographical Heatmap Component 87 Duo Equivalent The Duo version of this component features geographical heatmaps for both result sets as well as a comparison map highlighting country by country which of the two query tag
253. ything other than Time 9 Play Chart As Animation Animate the graph points over time Use the slider to the left of the play button to change playback speed 10 Enlarge Trends Component The Trends Component contains much detail In order to better use it click this button for the component to occupy all space in the component area 11 Size Variable The size of each node can be used to represent a fourth variable Available options are Same Size Countries Unique Users and Occurrences 12 Use Trails Show trails as each node is animated Use in conjunction with the time slider to create ad hoc line graphs 13 Chart Area Area used to display results Purpose The Trends Component provides multidimensional views of hashtag usage over a temporal range Each hashtag can be pivoted on number of occurences number of unique users tweeting the tag number of unique countries in which the tag is being used and by usage date Colour and shape can also be used to represent any of these variables Tags can be viewed as an animated scatter chart a bar chart or a line graph The dataset returned for each query can be filtered using the temporal query options in the appli cation Navigation Bar Hashtags can be whitelisted or blacklisted and limits can be placed on the number of occurences a tag must have for it to be appear within the component Users can track topics over time observe tag patterns of usage does t
254. ze at any given time with a series of preview windows along the bottom of the screen used to switch between compo nents This is an excellent suggestion particular for low resolution monitors and would be worth investigating further given additional development time Other negative terms used such as overwhelming and confusing emphasised that users need to be introduced to each component in a carefully managed way to prevent mental overload The current online help in Pulse is functional but basic adding a tutorial video or animation overlay on first use could help to alleviate the initial mental workload of understanding the function of each component Certain words chosen were applicable to very specific situations For example counter inituitive was picked by one participant due to the way time queries are filtered using a tag whitelist rather than typing tags in the query box Another example of this was the term exciting which was specifically directed towards the Trends Component shown after a successful time query Though the word list is unmatched in understanding user sentiment there are inconsistencies which 135 are difficult to resolve particularly where participants interpret words differently For example 4 participants rated the the application as fast whilst 1 judged it to be slow Similarly 2 par ticipants described the application as friendly whereas one used the word inti

here - codinghands.co.uk

Contents

Download Pdf Manuals

Related Search

Related Contents