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An Introduction to Social Mining Vladimir Gorovoy and Yana Volkovich @yvolkovich Barcelona Media, Information, Technology & Society Group Barcelona, Spain @vgorovoy Yandex, Yandex.Uslugi Saint Petersburg, Russia August,

  1. An Introduction to Social Mining Vladimir Gorovoy ∗ and Yana Volkovich † † @yvolkovich Barcelona Media, Information, Technology & Society Group Barcelona, Spain ∗ @vgorovoy Yandex, Yandex.Uslugi Saint Petersburg, Russia August, 15-19 2011 V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 1 / 35

  2. Outline Ranking Twitter 1 Location and social networks 2 V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 2 / 35

  3. Ranking Twitter Twitter Twitter is an online service that allows users to publish text-based post up to 140 characters (“tweets”). Twitter was launched in 2006; Now: 200 million users; 180 million tweets and 1.6 billion search queries per day V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 3 / 35

  4. Ranking Twitter Demographics Who? 5% of twitter users create 75% of the content; 54% of Twitter users are female, 46% users are male; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 4 / 35

  5. Ranking Twitter Pointless babble What? 40% of tweets are pointless babble (“I’m eating a sandwich”) [Pearanalytics, 2009] V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 5 / 35

  6. Ranking Twitter # followers (1) Ranking Twitter V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 6 / 35

  7. Ranking Twitter # followers (1) # followers; twittercounter.com/pages/100 V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 7 / 35

  8. Ranking Twitter # followers (2) spammers have far more followers than average users [Yardi et al., 2010]; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 8 / 35

  9. Ranking Twitter #followers/#followee 72% of users follow more than 80% of their followers; 80% of users have 80% of their friends follow them back. [Weng et al., 2010]. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 9 / 35

  10. Ranking Twitter #followers/#followee # followers # followee ratio; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 10 / 35

  11. Ranking Twitter #followers/#followee V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 11 / 35 listocomics.com/394-piramide-del-glamour-twittero/

  12. Ranking Twitter #followers/#followee ratio: Oprah: 1.67 ∗ 10 5 ; CNN Breaking News: 1.04 ∗ 10 5 ; Lady Gaga 18.08; from [Gayo-Avello and Brenes, 2010]; discounted ratio = # followers − reciprocal # followee − reciprocal V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 12 / 35

  13. Ranking Twitter Other techniques PageRank; TunkRank; TwitterRank; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 13 / 35

  14. Ranking Twitter TunkRank TunkRank by Daniel Tunkelang ( tunkrank.com ) assumptions: (1) every user has a given influence that is a numerical estimator of the number of people who will read his tweets; (2) users’ attention to their followees is equally distributed; (3) user X will retweet a tweet by user Y with defined probability p retweet ; just 2% of tweets are retweets (Dan Zarrella, “The science of ReTweets report”); 2,87% in [Gayo-Avello and Brenes, 2010]. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 14 / 35

  15. Ranking Twitter TunkRank TunkRank: � 1 + p retweet ˙ � Influence ( Y ) � p notice Influence ( X ) = | Following ( Y ) | Y ∈ Followers ( X ) p notice is total attention of the user devoted to Twitter; p retweet is retweet probability; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 15 / 35

  16. Ranking Twitter Twitter rank TwitterRank [Weng et al., 2010]: to rank users separately for different topics PageRank + topical similarity between users; V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 16 / 35

  17. Ranking Twitter Comparison comparison [Gayo-Avello and Brenes, 2010]: V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 17 / 35

  18. Twitter study Spanish revolution 15-M Movement : series of peaceful demonstrations in Spain. several weeks of sit-ins in 58 cities. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 18 / 35

  19. Location-based social networks Introduction Location and social networks. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 19 / 35

  20. Location-based social networks Introduction People will to share their location. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 20 / 35

  21. Location-based social networks Obama joins Foursquare V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 21 / 35

  22. Location-based social networks Obama joins Foursquare V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 22 / 35

  23. Location-based social networks Facebook connections http://paulbutler.org/archives/ visualizing-facebook-friends/ V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 23 / 35

  24. Location-based social networks Tuenti connections http://beautyofsocialnetworks.blogspot.com/2011/02/ visualizing-spains-friendship.html V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 24 / 35

  25. Location-based social networks Social ties and geographic distances Social ties and geographic distances Popular assumption: individuals try to minimize the efforts to maintain a friendship by interacting more with their spatial neighbors. online tools and long-distance travel might result in the ‘death of distance’. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 25 / 35

  26. Location-based social networks Social ties and geographic distances Flickr [Crandall et al., 2010]; in 60% cases: users are friends if they have 5 co-occurrences within a day (in distinct cells with sides equal to 1 latitude-longitude degree). V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 26 / 35

  27. Location-based social networks Social ties and geographic distances Probability of a friendship between two individuals as a function of their geographic distance. (a) livejournal [Liben-Nowell et al., (b) facebook [Backstrom et al., 2005] 2010] V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 27 / 35

  28. Location-based social networks foursquare & gowalla from [Scellato et al., 2011] V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 28 / 35

  29. Location-based social networks foursquare & gowalla Friends tend to be much closer than random users: about 50% of social links span less than 100 km, while about 50% of users are more than 4 000 km apart. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 29 / 35

  30. Location-based social networks foursquare & gowalla Probability of friendship vs. geographic distance V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 30 / 35

  31. Location-based social networks facebook user characteristics vs. location sharing and responds [Chang and Sun, 2011] V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 31 / 35

  32. Location-based social networks facebook to predict next check-in strongest: the number of previous check-ins by the user; significant: is the number of check-ins previously made by friends; small but significant: the day; not significant: the day of week. to predict response significant: the distance between the user (comments) and the actor (checks-in); the actor is near the user → the likelihood of a comment goes up dramatically. V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 32 / 35

  33. Questions Questions V. Gorovoy & Y. Volkovich (Yandex & BM) SocM: RuSSIR/EDBT 2011 Summer School August, 15-19 2011 33 / 35

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