Utilizing social annotations for topical search in Twitter Saptarshi

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 Socio-technological networks in offline world Indian Railway Network Traffic analysis

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

How to discover topical attributes? Prior attempts rely on contents of tweets or userprofiles [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

Mining Lists to infer topics Collect Lists containing a given user U Identify U’s topics from List metadata Basic IR techniques such as casefolding, remove domain-specific stopwords Extract nouns and adjectives

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

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

Who-is-who service Developed a Who-isWho service for Twitter Shows word-cloud for major topics for a given user http://twitter-app.mpi-sw s.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.

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

Cognos results for “politics”

Cognos results for “stem cell”

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.

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?

Topics in Twitter – major topics to niche ones

Study on the Indian Railway Network

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

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

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

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.

Thank You Questions / Suggestions?

Backup slides

Cognos vs. Twitter Who-ToFollow