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Utilizing social annotations for topical search in Twitter

Utilizing social annotations for topical search in Twitter. Saptarshi Ghosh BESU Shibpur Complex Network Research Group CSE, IIT Kharagpur. General overview. Social networks in online world Twitter, folksonomies such as Delicious Modeling the network evolution Improving search services

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Utilizing social annotations for topical search in Twitter

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  1. Utilizing social annotations for topical search in Twitter Saptarshi Ghosh BESU Shibpur Complex Network Research Group CSE, IIT Kharagpur

  2. General overview • Social networks in online world • Twitter, folksonomies such as Delicious • Modeling the network evolution • Improving search services • Socio-technological networks in offline world • Indian Railway Network • Traffic analysis

  3. Topical attributes of Twitter users • Twitter has emerged as an important source of information & real-time news • Increasing access through topical search [Teevan WSDM 2011] • Motivation: to discover topical attributes / expertise of users • Potential applications • Know credentials of a user • Identify topical experts

  4. How to discover topical attributes? • Prior attempts rely on contents of tweets or user-profiles [Ramage ICWSM 2010, Pochampally SIGIR Workshop 2011] • Many profiles do not give topical information • Tweets often contain day-to-day conversation  difficult to infer topics [Java SNA-KDD 2007, Wagner SocialCom 2012] • Proposed methodology • Use social annotations – how a user is described by others • Social annotations gathered through Twitter Lists

  5. Mining Lists to infer topics • Collect Lists containing a given user U • Identify U’s topics from List meta-data • Basic IR techniques such as case-folding, remove domain-specific stopwords • Extract nouns and adjectives

  6. Topics inferred from Lists politics, senator, congress, government, republicans, Iowa, gop, conservative politics, senate, government, congress, democrats, Missouri, progressive, women linux, tech, open, software, libre, gnu, computer, developer, ubuntu, unix

  7. Lists vs. other features Profile bio love, daily, people, time, GUI, movie, video, life, happy, game, cool Most common words from tweets Most common words from Lists celeb, actor, famous, movie, stars, comedy, music, Hollywood, pop culture

  8. Who-is-who service • Developed a Who-is-Who service for Twitter • Shows word-cloud for major topics for a given user • http://twitter-app.mpi-sws.org/who-is-who/ N. Sharma, S. Ghosh, F. Benevenuto, N. Ganguly, K. Gummadi,Inferring who-is-who in the Twitter social network, WOSN 2012.

  9. Search system for topic experts • Cognos, a search system for topic experts http://twitter-app.mpi-sws.org/whom-to-follow/ • Given a query (topic) • Identify users related to the topic using Lists • Rank identified users • Uses ranking scheme based on Lists • Relevance of user to query • Popularity of user

  10. Cognos results for “politics”

  11. Cognos results for “stem cell”

  12. Evaluation of Cognos • Evaluations through user-surveys • Cognos gives accurate results for wide variety of queries • Cognos vs. Twitter Who-To-Follow service • Judgment by majority voting • Out of 27 queries, Cognos judged better for 12, Twitter WTF better for 11 and tie for 4 S. Ghosh, N. Sharma, F. Benevenuto, N. Ganguly, K. Gummadi,Cognos: Crowdsourcing Search for Topic Experts in Microblogs, SIGIR 2012.

  13. Twitter as a source of information • Characterizing the experts in Twitter  characterizing Twitter platform as a whole • What are the topics on which information is available on Twitter?

  14. Topics in Twitter – major topics to niche ones

  15. Study on the Indian Railway Network

  16. Motivation: rail accidents during 2010 • Details of accidents: in Wiki page on IR accidents • Considered only accidents due to • Collision between trains • Derailment

  17. IRN data collection • Crawled schedules of express trains from www.indianrail.gov.in in October 2010 • 2195 express train-routes, 3041 stations • Scheduled time of each train reaching each station • Express train schedules for several years since 1991 • From Trains At A Glance time-tables • Obtained from National Rail Museum, New Delhi

  18. Observations • Many trunk-routes in the Indo-Gangetic Plain (IGP) have high daily traffic with low headway • Bad scheduling of IR traffic • Routes in north India have especially low headway during early morning hours when dense fog is likely • Skewed distribution of daily traffic • Unbalanced growth of traffic in IGP • Traffic in some segments in IGP has increased by 250% in 2009, compared to the traffic in 1991 • Very low construction of new tracks

  19. Publication and press coverage S. Ghosh, A. Banerjee, N. Ganguly. Some insights on the recent spate of accidents in Indian Railways. Physica A, Elsevier, 2012.

  20. Thank You Questions / Suggestions?

  21. Backup slides

  22. Cognos vs. Twitter Who-To-Follow

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