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Investigating Alternative Forms of Search

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A talk given at UCL in Feb 2010

A talk given at UCL in Feb 2010

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  • <br />
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  • 10 mins backround <br /> 15 mins study <br /> 20 mins twitter <br />
  • 10 mins backround <br /> 15 mins study <br /> 20 mins twitter <br />
  • what do i mean by alternative forms of search? <br />
  • what do i mean by alternative forms of search? <br />
  • 199 millions pages that could help you! <br />
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  • completeness - including no answer <br />
  • this is how ive been investigating it <br /> <br /> grounding it in mspace <br />
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  • google <br />
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  • better video? <br />
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  • but i suffered the result-availability problem <br />
  • wanted a quick method - applied to prototyes - earlier design <br /> <br /> bit like stephann makri&#x2019;s info behaviour work, but at a lower seeking only level. <br />
  • 10 mins? earlier? <br />
  • so what did i do? <br /> <br /> dont dwell <br />
  • so what did i do? <br /> <br /> dont dwell <br />
  • so what did i do? <br /> <br /> dont dwell <br />
  • so what did i do? <br /> <br /> dont dwell <br />
  • so what did i do? <br /> <br /> dont dwell <br />
  • so what did i do? <br /> <br /> dont dwell <br />
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  • how does it support tactics at each of these points <br />
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  • and then it uses the models to infer support for certain types of users. <br />
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  • hit at 18mins ish <br />
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  • not going to go into history - ann knows the history <br />
  • these have increasing ecological validity <br />
  • but that doesnt tell you if its a good method for practitioners <br /> <br /> not &#x2018;recommended&#x2019; - tried with very newbies <br />
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  • Cockton - its not measuring time - effort <br /> but makes you think about each feature and how it *could* be designed <br /> doesnt impose design - or design decisions <br /> &#x2018;moves&#x2019; is just not a suitable measure for that <br />
  • prof keith lloyd - director health sciences swansea <br /> <br /> analyse current UI <br />
  • try to hit at 25 mins <br />
  • that was a bit of a large vision into the future <br /> <br /> but in the mean time - it turns out that there&#x2019;s still a lot we dont know about these exploratory conditions for example. <br />
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  • information need is probably either: <br />
  • information need is probably either: <br />
  • information need is probably either: <br />
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  • actually its still going <br />
  • 30 mins or earlier <br />
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  • only 10 of the 12 words <br />
  • left col - trouble? <br /> right col - children! <br />
  • browsing - its very time-passing - open ended <br /> exploring - in a fixed space. <br /> <br /> faceted browsing? <br />
  • hunting will provide us with a lot of insight into the information scarcity problem <br />
  • searching - not associated with time - but often with &#x2018;best&#x2019; or &#x2018;optimal&#x2019; answers <br /> seeking - people (inc porn) and truth/god etc <br /> <br /> investigating was never really associated with love, happy, or goodness <br /> but the police <br />
  • tried tweets with emoticons and a &#x2018;lingo training set&#x2019; <br />
  • hunting often negative - perhaps lots of fails? <br /> browsing is neutral <br /> exploring is positive? <br />
  • exploring is more positive, and browsing is very neutral <br /> browsing often to pass time <br />
  • positive investigation == successful results? <br /> hunting, however, includes aptlist in the negative <br />
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  • retrieval is procedureal - perhaps the driving force of the IR community? <br /> &#x2018;faceted browsing&#x2019; - browsing is actually just open ended passing time <br /> - chaining and random paths <br /> ES - understanding the breadth of something or checking them all <br /> Inv. - hidden dependancies (ben s) <br />
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  • 40 mins <br />
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Investigating Alternative Forms of Search Investigating Alternative Forms of Search Presentation Transcript

  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Investigating Alternative Forms of Search Max L. Wilson Future Interaction Technologies Lab Swansea University www.fitlab.eu m.l.wilson@swansea.ac.uk Max L. Wilson m.l.wilson@swansea.ac.uk
  • Today BACKGROUND Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Today BACKGROUND Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Alternative Forms of Search What am I Does the What words looking for? site have it? should I use? White, R. W., Kules, B., Drucker, S. M., and schraefel, m. c. (2006). Introduction. Communications of the ACM, 49(4):36–39. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Alternative Forms of Search What am I Does the What words looking for? site have it? should I use? White, R. W., Kules, B., Drucker, S. M., and schraefel, m. c. (2006). Introduction. Communications of the ACM, 49(4):36–39. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Alternative Forms of Search What am I Does the What words looking for? site have it? should I use? White, R. W., Kules, B., Drucker, S. M., and schraefel, m. c. (2006). Introduction. Communications of the ACM, 49(4):36–39. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Exploratory Search Marchionini, G. (2006). Exploratory search: from finding to understanding. Communications of the ACM, 49(4):41–46. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Ben Shneiderman added... • Multi-session searches • Life long learning • Completeness searches • Low result availability Shneiderman, B. HCIR2009 Keynote: The Future of Information Discovery Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • mSpace (early version) schraefel, m. c., Wilson, M. L., Russell, A., and Smith, D. A. (2006). mspace: improving information access to multimedia domains with multimodal exploratory search. Communications of the ACM, 49(4):47–49. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Especially iTunes Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Especially iTunes Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • The problem was... • We were measuring mSpace from an HCI perspective • But not from Information Seeking perspective Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • What I wanted Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • My research agenda became • Produce a method that can be quickly applied to Search UIs • ‘What types of search does this support, and how well?’ and ‘Which types of users, therefore, will this be good for?’ • My goal was/is “improving search interfaces” Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • I spend a few years making the method I wanted Bates, M. J. (1979a). Idea tactics. Journal of the American Society for Information Belkin, N. J., Marchetti, P. G., and Cool, C. Science, 30(5):280–289. (1993). Braque: design of an interface to support user interaction in information retrieval. Bates, M. J. (1979b). Information search Information Processing and Management, 29(3): tactics. Journal of the American Society for 325–344. Information Science, 30(4):205–214. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • I spend a few years making the method I wanted Wilson, M. L., schraefel, m. c., and White, R. W. (2009). Evaluating advanced search interfaces using established information-seeking models. Journal of the American Society for Information Science and Technology, 60(7):1407–1422. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Some Validation Steps •6 people judged/critiqued/improved the model connections • Created similar assessments to user studies • Abdi even tried a very early version and found some holes! Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Come and Sii what I’ve built http://mspace.fm/sii Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Graph 1: Tactics What is design A good at? What do they suck at? What don’t any of the designs do at all? Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Graph 2: UI Features This is the better implementation But this feature of this feature was good in this design You only put this feature in 1 design! and it contributes a lot of support. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Graph 3: Searcher Types • If the pattern is in halves, then its because of Dimension 1: Method • If the pattern is in the quarters, then its Dimension 2: Goal Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • So when to use this Eval. Method? • Compare multiple design prospects • Check a re-design - make sure its actually better • Compare your design to an established one • Investigate why people don’t use part of your design • Use it to understand what makes a popular service so good • Understand how a new feature might help Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Evaluating New Eval. Methods 1. There is no good way to show that one evaluation method is better than another 2. Real use by real practitioners is the best to demonstrate if/ when a method has value 3. You can only run these case studies in ecologically-valid circumstances Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • My On-Going Approach • 1) Tried to break the usability • 2) Practitioner Interviews • 3) Forthcoming case study (... studies) • 4) Release to the UX community Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 1) In trying to break it Testing • This is not on the ‘recommended’ list of approaches Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 2) Practitioner Interviews • Worked with 1) a UI Consultant and 2) an Information Architect • Generated some Sii analyses together • Interviewed them for how/where the saw Sii adding value • In Furniss’ 4 contexts of use good fit | relationships | communication | related tasks Furniss, D., Blandford, A., and Curzon, P. (2008). Towards Maturing Usability Practice in Website Design: Grounding how practitioners work to inform research requirements, chapter 7, pages 144–167. Human- Computer Interaction Series. Springer- Verlag. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 1) 2) 3) Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 2) Some Benefits • Consultant - Prioritisation • Consultant - Evidence of Reasoning/Planning • Consultant - Provide Prior Examples • I-Architect - Enforced Rigorous Planning • I-Architect - Enforced Rigorous Checking • Both - Could fit easily into practice + add value • G. Cockton - Forces you to think - its the journey Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 3) Forthcoming Case Study/ies + Sii + Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • 4) Better Tool Development Sapient UX Designer Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • And now to something very different • What do we really know about real human information needs? Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • And now to something very different • What do we really know about real human information needs? Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Real Searching Tasks • what do we really know? • the community is still deciding what makes up different ‘hard searching scenarios’ • and the best we’ve had so far is examples from search engine logs • and queries != needs Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Query vs Information Need Query: Java Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Query vs Information Need Query: Java Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Query vs Information Need Query: Java Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Query vs Information Need Query: Java Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Interesting Examples Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • More Examples Wilson, M. L. (2009) Using Twitter to Assess Information Needs: Early Results. In: HCIR'09, 23rd October 2009, Washington DC. pp. 109-112. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • The Full Study •5 Months • Hourly Queries • 12 Terms (past/present/future tense) • Collected 100 latest Queries/Authors/DateTime • 800MB of Tweets • Thats 2.4M unique tweets from 1.7M unique users Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Analysis in 3 Stages • 1) Word-level statistical analysis • 2) Taxonomic Generation • 3) Topical Collection Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • used for the remainder of the analyses. Notably, however, this impaired. process also naturally occurring repetition, such as ‘exploring twitter’ which was found 1830 times by separate individuals; Several additional factors can be seen from the time-series usually those who had just created accounts. analysis. First, certain terms, such as ‘finding’, appear to have a weekly cyclical dip in usage over the weekend. Conversely, the 3.1.4 Corpus Characteristics use of the word studying rises at weekends, usually, however, in Time-series of data In total, as shown in Table 1, 3.7 million tweets were collected from nearly 1.7 million unique authors. Of these, around 2.4 million were unique tweets. RTs, or ReTweets, are a common form of rebroadcasting another person’s tweet to one’s own reference to exam preparation as discussed in the following sections. Figure 1 also highlights, perhaps more clearly than Table 1, the relatively infrequent use of the terms ‘retrieving’ and ‘foraging’. While foraging is perhaps less common in the English language, the word ‘retrieving’ and its variants are core to how the followers. Typically these are identified by the community-norm of beginning the tweet with the letters ‘RT’ and the source community describes searching. The use of the word ‘retrieve’ is person’s Twitter username [2]. The RT count produced was based discussed further in Section 4. on tweets that followed this typical pattern. Although counted for Figure 1: Time series analysis of the collected tweets, noting the 5 day system outage. Notice the downtime :( Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Word-level methods • 1) Log-likelihood Analysis (and inverse) • Words that are statistically likely to appear with each word compared to the whole corpus • 2) Semantic Neighbour Analysis • Words that are commonly expressed before and after each term Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • PARTMENT Chatting Willing MURDER WEB Lil O Experience Need OFFICIALS LOVE Churning OUSE Vodafone Feds PROBE HIDE Apple g/Explored Look/Looking/Looked OLICE B NGINE DE Usage Twitter Peaks Complexities Payments Take Wife Cooler Retrieving FORWARD POLICE GOOD TWITPIC LOL GOOGLE ENGINE NEWS Forward Like Back Freelance PARTMENT Handing Badgers HAIR HIDE Good ESULTS American Nicer CUTE JOB Interface OL Streets Just PIC TEST Hour EER Tourism Aww EYES APARTMENT Combinations CAVENGER Cooler Definitely LMAO SCAVENGER Pampered XAM Islamic Boots PRETTY WEB Appoint Found Retrieve/Retrieved/Retrieving ICE Out Trying DATA JOB Some GINE Someone How PASSWORD FORWARD Them OGLE Cross Could MMS ENGINE Password E Guilty Managed PW POLICE Jump B Entertaining Please AICENT GOOGLE Those Nemo Able LOST HIDE Messages R Funny Methane PHONE APARTMENT Whipped WARD Nine Gonna DELETED SOCIAL Quote Somewhere Finally ACCOUNT SEO Children IMIZATION Reaching Footage CAR JOBS Tennis /Foraged Search/Searching/Searched Food Job ENGINE FORWARD Engine OGLE Group Library GOOGLE HIDE Results TTER Disaster Twitter SEO TOMORROW Destroy B Some Advanced RESULTS DAY Function Themselves Talent TWITTER DEER Continues LINE Wild Accidentally MARKETING SCAVENGER Bar ONE Lunch Loses BING TODAY Feature WS Videos Document YAHOO EXAM Ends Max L. WilsonCommented ARTMENT Visual TOP TONIGHT Column m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Browsing vs Exploring Table 2: Seed terms above 1) preceding semantic neighbours [<-], 2) log-likelihood [+] , 3) inver semantic neighbours [->]. The terms Scanning and Studying were excluded from this table, for CT and Virus (for Scanning) and exams (for Studying). Terms are ordered by statistical sign <- + - -> <- + Browse/Browsing/Browsed Investigate/In Currently WEB POLICE Through Being POLICE Web XBOX JOB My Police DEATH Come LIVE ENGINE Youtube Still SHOOTING Tabbed PROFILE FORWARD Asos Authorities ACORN Now INTERNET MAN Internet Detectives FBI Anonymous FIREFOX TOMORROW Folder Sinai FIRE Tend NATION HIDE Download Going AUTHORIT Buds NETFLIX APARTMENT Chatting Willing MURDER Offer QUEUE SEO Experience Need OFFICIALS Go INSTANT HOUSE Vodafone Feds PROBE Explore/Exploring/Explored Look Flickr VIDDLER POLICE Usage Payments FORWAR Trying WORLD JOB Twitter Take GOOD Karaoke OPTIONS ENGINE Peaks Wife TWITPIC Want FLICKR HIDE Complexities Cooler LOL Isles CITY APARTMENT Handing Badgers HAIR Tabs VIDEOS RESULTS American Nicer CUTE Ready TRAVEL LOL Streets Just PIC Comedies POSSIBILITIES DEER Tourism Aww EYES Nineties ACHIEVEMENT SCAVENGER Cooler Definitely LMAO Exhibition DISCOVER EXAM Islamic Boots PRETTY Find/Finding/Found Retrieve/ Just HARD POLICE Out Trying DATA Trying LOL ENGINE Someone How PASSWORD Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Exhibition DISCOVER EXAM Islamic Boots PRETTY WEB Find/Finding/Found Retrieve/Retrieved/R Just HARD POLICE Out Trying DATA JOB Trying LOL ENGINE Someone How PASSWORD FORW Foraging and Hunting Gotta FUNNY GOOGLE Cross Could MMS ENGIN Cant LOST HIDE Guilty Managed PW POLIC Need LOVE WEB Entertaining Please AICENT GOOG Accidentally FINALLY JOB Nemo Able LOST HIDE Ever DEAD DEER Funny Methane PHONE APAR Officials HOPE FORWARD Nine Gonna DELETED SOCIA You TRYING SEO Somewhere Finally ACCOUNT SEO Nevermind PLACE OPTIMIZATION Reaching Footage CAR JOBS Forage/Foraging/Foraged Search/Searching/S Off FOOD JOB Food Job ENGINE FOR Urban BADGER GOOGLE Group Library GOOGLE HID Go MUSHROOMS TWITTER Disaster Twitter SEO TOM While WILD WEB Some Advanced RESULTS DAY Hunt URBAN LOL Themselves Talent TWITTER DEE Must BERRIES ONLINE Wild Accidentally MARKETING SCA Information BRUSHES IPHONE Lunch Loses BING TOD Content DINNER NEWS Videos Document YAHOO EXA Livestock KITCHEN APARTMENT Commented Visual TOP TON Verve BEES HAHA Presentation Google SOCIAL TES Hunt/Hunting/Hunted Seek/Seeking/So Apartment JOB GOOGLE Red Hide HIDE GOO Scavenger APARTMENT ENGINE Down Client JOBS ENG Job DEER FORWARD Some Police GOD FOR Treasure SCAVENGER WEB Colombia Desperately MANAGER TWI Witch TREASURE SEO Ski Who ATTENTION TOM Deer GHOST TWITTER Snark System AMY LOL House HOUSE HIDE Gazelle Companies EXPERIENCED APA Bargain APTLST TEST Season Currently CLIENT BAC Ghost SEASON OPTIMIZATION Simpsons Originality SALES GON Cannot DUCK POLICE Plans We SENIOR GOO Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • E Someone How PASSWORD FORWARD Them E Cross Could MMS ENGINE Password Guilty Managed PW POLICE Jump Entertaining Please AICENT GOOGLE Those Searching and Seeking Nemo Able LOST HIDE Messages Funny Methane PHONE APARTMENT Whipped RD Nine Gonna DELETED SOCIAL Quote Somewhere Finally ACCOUNT SEO Children ZATION Reaching Footage CAR JOBS Tennis aged Search/Searching/Searched Food Job ENGINE FORWARD Engine E Group Library GOOGLE HIDE Results ER Disaster Twitter SEO TOMORROW Destroy Some Advanced RESULTS DAY Function Themselves Talent TWITTER DEER Continues E Wild Accidentally MARKETING SCAVENGER Bar E Lunch Loses BING TODAY Feature Videos Document YAHOO EXAM Ends MENT Commented Visual TOP TONIGHT Column Presentation Google SOCIAL TEST Perfect ted Seek/Seeking/Sought E Red Hide HIDE GOOGLE Out E Down Client JOBS ENGINE After RD Some Police GOD FORWARD Amy Colombia Desperately MANAGER TWITTER Experienced Ski Who ATTENTION TOMORROW Me ER Snark System AMY LOL Lord Gazelle Companies EXPERIENCED APARTMENT Liberation Season Currently CLIENT BACK Principals ZATION Simpsons Originality SALES GONNA Different Plans We SENIOR GOOD Truth Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Word-Level Methods • Sentiment Analysis - the difference between happy and sad tweets • Built a NaiveBayes classifier • Difficult to rate tweets this way - too short • 7 Humans couldnt agree (60% agreement - Fleiss 0.386) • Majority Vote used to judge accuracy of the classifier • Best results from academic training data with 95% confidence threshold. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • shown in Table 3. Further to the previous Log-Likelihood hese human- analysis, the training bag of positive and negative terms were created by added to the stop word list, to make sure that the terms shown in Happy vs Sad Tweets formal terms Table 3 were not simply those used to divide the corpus. tive/negative First, we see from Figure 2 that ‘exploring’, ‘finding’, and identified by ‘looking’ each have more positive connotations than neutral or ted by Go et negative. While we might expect ‘looking’ to be associated with In line with ‘looking good’, it is perhaps more surprising that exploring and with limited Wilson et al rformed best k=0.548, or to success tweets [13]. nt on human using mixed or negative in Figure 2. positive and Figure 2: Breakdown of the sentiment of tweets per seed term. Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Browsing vs Exploring Table 3: Positive and Negative keywords in each seed-term sub-corpus calculated using th p<0.0001). The ‘Foraging’ sub-corpus was too small to identify 10 sig Positive Negative Positive Negative Positive Negat Browse/Browsing/Browsed Explore/Exploring/Explored Find/Finding/Found XBOX Bored Time Stressed Time Bored Nation Air Learn Mars Thought Body Playing Surprises Opportunities Bored Person Police Comment Amazon Earned Ruins Coming Missing Instant Shooting Kids National Hoping Breakin Queue International Human Mysteries Wondering News Netflix Owners Nature Comcast Gift Limits Ago Adobe Day Limits Learn Murder Save Kindle Wonders NBC Deals Throws Time National Living Side Wanted Secret Hunt/Hunting/Hunted Investigate/Investigating/Investigated Look/Looking/Look Time Police Time Police Time Bored Tweeps Aptlist Balled Man Thought Police Season Bored Jessica Alleged Person Hurts Bonnie Man Sounds Shooting Voted Stresse Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Police Time Police Time Bored Aicent Ac Aptlist Balled Man Thought Police Time Bo Bored Jessica Alleged Person Hurts Net Fe Man National Killed Sounds Thought Human Searching vs Seeking Shooting Secret Officials Voted Coming Vote Stressed National Offense PW MMS ID Re Od En Missing Hooker Killed Gift Ass Days Cr News General Authorities Kids Question Information Tra Cops State Woman Christmas Obsessed Managed Sla Suspects Verified Stabbing Loved Air Thought Gi g/Scanned Search/Searching/Searched Seek/Seeking/Sought Study/Studying/ Business Optimization Police Manager Police Time Bo Risks Engine Missing Time Suspects Learn Di Bored SEO Suspects God Accused Bible Fin Computer Time Man Chat Robbery Brain Ob England Learn Bored Health Alleged Thought Na Radiation Forum Air Person Shooting Education De Diseases Thought Robbery Personal Prosecutors Practice Br CT Person Authorities Resources News Human Hu Sunderland Manager News Education Man Finished Str Reader Times Awards Human Damages Coming Di nsequently, the majority judgement of each tweet negative terms, calculated with the Log-Like ate the sentiment module. shown in Table 3. Further to the previous aining corpora were tested against these human- analysis, the training bag of positive and negat aMax L. Wilson bag of positive/negative words created by added to the stop word list, to make sure that the m.l.wilson@swansea.ac.uk csmax@swan.ac.uk Table 3 were not simply those used to divide the c
  • Some implications so far • Information Retrieval - should be as close to fetching as poss • (Faceted) Browsing - should be chaining not refining/narrowing • Exploratory Search - should be bounded not unknown • Investigating - not just what + but also the ‘why’ • Studying - life long learning examples • Hunting - information availability examples Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • On-Going work: Taxonomy • want to better understand the different searching scenarios • going to use grounded theory • collaborating with David Elsweiler in Erlanger University • produce a taxonomy of these scenarios, with examples Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • On-Going Work: Topical • With the taxonomy, we want to extract concrete examples • Want to leverage the taxonomy and some automatic methods • And produce a topical analysis of those scenarios Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • In the mean time... wanna play? http://www.cs.swan.ac.uk/~csmax/twitter/ Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk
  • Questions? Max L. Wilson m.l.wilson@swansea.ac.uk csmax@swan.ac.uk