Utilizing social annotations for topical search in Twitter
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Transcript of Utilizing social annotations for topical search in Twitter
Utilizing social annotations for topical search in Twitter
Saptarshi Ghosh
BESU Shibpur
Complex Network Research GroupCSE, 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 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
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
Topics inferred from Lists
linux, tech, open, software, libre, gnu, computer, developer, ubuntu, unix
politics, senator, congress, government, republicans, Iowa, gop, conservative
politics, senate, government, congress, democrats, Missouri, progressive, women
Lists vs. other features
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
Profile bio
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.
Search system for topic experts Cognos, a search system for topic expertshttp://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 coverageS. 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-To-Follow