Introduction Socialbot Challenge Success Measures Results ConclusionsUnderstanding The Impact Of Socialbot Attacks InOnlin...
Introduction Socialbot Challenge Success Measures Results ConclusionsIntroductionWhat is a socialbot?• A socialbot is a pi...
Introduction Socialbot Challenge Success Measures Results ConclusionsResearch QuestionTo what extent and how can socialbot...
Introduction Socialbot Challenge Success Measures Results ConclusionsResearch QuestionCan socialbots animate previously un...
Introduction Socialbot Challenge Success Measures Results ConclusionsThe Experiment: Socialbot ChallengeSocialbot challeng...
Introduction Socialbot Challenge Success Measures Results ConclusionsDataset• Tweets of targets and bots during control (3...
Introduction Socialbot Challenge Success Measures Results ConclusionsControl and Experimental Phases19.09 22.10. 12.11.con...
Introduction Socialbot Challenge Success Measures Results ConclusionsMeasuring the Success of SocialbotsPacSocial found li...
Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recom...
Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recom...
Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recom...
Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediato...
Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediato...
Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediato...
Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediato...
Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediato...
Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7....
Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7....
Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7....
Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7....
Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmed...
Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmed...
Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmed...
Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmed...
Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmed...
Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation be...
Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation be...
Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation be...
Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation be...
Introduction Socialbot Challenge Success Measures Results Conclusions[1] Lars Backstrom and Jure Leskovec. “Supervised ran...
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The Impact of Socialbots in Online Social Networks

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The Impact of Socialbots in Online Social Networks

  1. 1. Introduction Socialbot Challenge Success Measures Results ConclusionsUnderstanding The Impact Of Socialbot Attacks InOnline Social NetworksSilvia Mitter, Claudia Wagner, Markus StrohmaierKnowledge Technologies InstituteGraz University of TechnologyACM Web ScienceMay 4, 2013Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 1 / 13
  2. 2. Introduction Socialbot Challenge Success Measures Results ConclusionsIntroductionWhat is a socialbot?• A socialbot is a piece of software that controls a user account in anonline social network and passes itself of as a human beingThe danger of socialbots• Harvest private user data• Spread misinformation and influence users• Boshmaf et al. [2] show that Facebook can be infiltrated by socialbots sending friend requests. Average reported acceptance rate:35,7% up to 80% depending on how many mutual friends the socialbots had with the infiltrated users.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 2 / 13
  3. 3. Introduction Socialbot Challenge Success Measures Results ConclusionsResearch QuestionTo what extent and how can socialbots manipulate the link creationbehavior of users in OSN?Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 3 / 13
  4. 4. Introduction Socialbot Challenge Success Measures Results ConclusionsResearch QuestionCan socialbots animate previously unconnected users to connect?Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 4 / 13
  5. 5. Introduction Socialbot Challenge Success Measures Results ConclusionsThe Experiment: Socialbot ChallengeSocialbot challenge on Twitter organized by the Pacific Social ArchitectureGroup [3].• Bots: 9 bots using same strategies with some variance• Aim: manipulate users’s link creation behavior – i.e., animatepreviously unconnected users to connect• Target groups: 9 groups, each consisting of 300 partially sociallyinterlinked users• Control Phase: how many new links are usually created betweentarget users?• Experimental Phase: how many new links are created between targetusers if socialbots are active?• Success: PacSocial reported a link creation increase of 43% [3]Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 5 / 13
  6. 6. Introduction Socialbot Challenge Success Measures Results ConclusionsDataset• Tweets of targets and bots during control (33 days) and experimentalphase (21 days)• Social relations between targets and bots at different points in timeduring control and experimental phaseSocialbots 9Targets 2,700Number of Tweets 1,006,351Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 6 / 13
  7. 7. Introduction Socialbot Challenge Success Measures Results ConclusionsControl and Experimental Phases19.09 22.10. 12.11.control phase33 daysexperimental phase 121 dayscontrol phase33 days19.09 24.11.experimental phase 233 days22.10.PacSocial ExpModified ExpOct 26 2011 Nov 02 2011 Nov 09 2011 Nov 16 2011 Nov 23 20110100200300400500TweetCountTweets authored by botsFigure: Tweets authored by bots over time.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 7 / 13
  8. 8. Introduction Socialbot Challenge Success Measures Results ConclusionsMeasuring the Success of SocialbotsPacSocial found link creation increase of 43% during experimentalphase 1, which they attributed to the socialbots.Measure the impact of socialbots while controlling the impact of someobvious confounding variablesSuccess measures describe preceding situations of new link creation events,along two dimensions:• Recommendation Types: How is the link creation recommended?• Mediators: Who recommends the link creation?Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 8 / 13
  9. 9. Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recommendation Types)• RT 1 – Direct User Recommendation via Tweet• RT 2 – Indirect User Recommendation via Follow• RT 3 – Indirect User Recommendation via TweetUser A User BMediatorstarts followingcreates TweetincludesincludesFigure: RT 1Figure: RT 2 Figure: RT 3Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13
  10. 10. Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recommendation Types)• RT 1 – Direct User Recommendation via Tweet• RT 2 – Indirect User Recommendation via Follow• RT 3 – Indirect User Recommendation via TweetUser A User BMediatorstarts followingcreates TweetincludesincludesFigure: RT 1User A User BMediatorfollowsstarts followingfollowsFigure: RT 2Figure: RT 3Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13
  11. 11. Introduction Socialbot Challenge Success Measures Results ConclusionsSome ways in which new links can be recommended(Recommendation Types)• RT 1 – Direct User Recommendation via Tweet• RT 2 – Indirect User Recommendation via Follow• RT 3 – Indirect User Recommendation via TweetUser A User BMediatorstarts followingcreates TweetincludesincludesFigure: RT 1User A User BMediatorfollowsstarts followingfollowsFigure: RT 2User A User BMediatorfollowsstarts followingincludescreates TweetFigure: RT 3Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13
  12. 12. Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediator: Every user from the target group can act as humanmediator.• Socialbot Mediator: Every socialbot can act as socialbot mediator.• Human AND Socialbot Mediator: Preceding human and socialbotmediator actions can be measured before link creation.• No Measurable Mediator: No potential mediator can be identifiedfrom the data explaining the link creation.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 10 / 13
  13. 13. Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediator: Every user from the target group can act as humanmediator.• Socialbot Mediator: Every socialbot can act as socialbot mediator.• Human AND Socialbot Mediator: Preceding human and socialbotmediator actions can be measured before link creation.• No Measurable Mediator: No potential mediator can be identifiedfrom the data explaining the link creation.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 10 / 13
  14. 14. Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediator: Every user from the target group can act as humanmediator.• Socialbot Mediator: Every socialbot can act as socialbot mediator.• Human AND Socialbot Mediator: Preceding human and socialbotmediator actions can be measured before link creation.• No Measurable Mediator: No potential mediator can be identifiedfrom the data explaining the link creation.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 10 / 13
  15. 15. Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediator: Every user from the target group can act as humanmediator.• Socialbot Mediator: Every socialbot can act as socialbot mediator.• Human AND Socialbot Mediator: Preceding human and socialbotmediator actions can be measured before link creation.• No Measurable Mediator: No potential mediator can be identifiedfrom the data explaining the link creation.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 10 / 13
  16. 16. Introduction Socialbot Challenge Success Measures Results ConclusionsWho recommends a link (Mediator Types)• Human Mediator: Every user from the target group can act as humanmediator.• Socialbot Mediator: Every socialbot can act as socialbot mediator.• Human AND Socialbot Mediator: Preceding human and socialbotmediator actions can be measured before link creation.• No Measurable Mediator: No potential mediator can be identifiedfrom the data explaining the link creation.Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 10 / 13
  17. 17. Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55Basis for Calculations 3.36 4.91 4.21Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13
  18. 18. Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55Basis for Calculations 3.36 4.91 4.21Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13
  19. 19. Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55Basis for Calculations 3.36 4.91 4.21followsUser A User Bstarts followingUser A User Bstarts followingcommunicate(a)(b)Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13
  20. 20. Introduction Socialbot Challenge Success Measures Results ConclusionsLink CreationLink Creation ctr exp1 exp2Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55Basis for Calculations 3.36 4.91 4.21followsUser A User Bstarts followingUser A User Bstarts followingcommunicate(a)(b)Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13
  21. 21. Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmediatedhuman &socialbotmediatedundefinedmediatedabs % abs % abs % abs %control phase 1.49 44.14 0.00 0.00 0.00 0.00 1.88 55.86exp. phase 1 1.81 36.90 0.33 6.79 0.29 5.83 2.48 50.48exp. phase 2 1.46 34.54 0.49 11.51 0.49 11.51 1.79 42.45• Total increase of links in exp1 around 40% and around 20% in exp2.That means human’s link creation behavior increased. But why?• Around 50% of links can not be explained by preceding situation(external factors?)• Around 1/3 of all links have been recommended by human and only6-12% have been recommended by botsMitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13
  22. 22. Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmediatedhuman &socialbotmediatedundefinedmediatedabs % abs % abs % abs %control phase 1.49 44.14 0.00 0.00 0.00 0.00 1.88 55.86exp. phase 1 1.81 36.90 0.33 6.79 0.29 5.83 2.48 50.48exp. phase 2 1.46 34.54 0.49 11.51 0.49 11.51 1.79 42.45• Total increase of links in exp1 around 40% and around 20% in exp2.That means human’s link creation behavior increased. But why?• Around 50% of links can not be explained by preceding situation(external factors?)• Around 1/3 of all links have been recommended by human and only6-12% have been recommended by botsMitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13
  23. 23. Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmediatedhuman &socialbotmediatedundefinedmediatedabs % abs % abs % abs %control phase 1.49 44.14 0.00 0.00 0.00 0.00 1.88 55.86exp. phase 1 1.81 36.90 0.33 6.79 0.29 5.83 2.48 50.48exp. phase 2 1.46 34.54 0.49 11.51 0.49 11.51 1.79 42.45• Total increase of links in exp1 around 40% and around 20% in exp2.That means human’s link creation behavior increased. But why?• Around 50% of links can not be explained by preceding situation(external factors?)• Around 1/3 of all links have been recommended by human and only6-12% have been recommended by botsMitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13
  24. 24. Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmediatedhuman &socialbotmediatedundefinedmediatedabs % abs % abs % abs %control phase 1.49 44.14 0.00 0.00 0.00 0.00 1.88 55.86exp. phase 1 1.81 36.90 0.33 6.79 0.29 5.83 2.48 50.48exp. phase 2 1.46 34.54 0.49 11.51 0.49 11.51 1.79 42.45• Total increase of links in exp1 around 40% and around 20% in exp2.That means human’s link creation behavior increased. But why?• Around 50% of links can not be explained by preceding situation(external factors?)• Around 1/3 of all links have been recommended by human and only6-12% have been recommended by botsMitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13
  25. 25. Introduction Socialbot Challenge Success Measures Results ConclusionsResultsLink Creation RT 123 humanmediatedsocialbotmediatedhuman &socialbotmediatedundefinedmediatedabs % abs % abs % abs %control phase 1.49 44.14 0.00 0.00 0.00 0.00 1.88 55.86exp. phase 1 1.81 36.90 0.33 6.79 0.29 5.83 2.48 50.48exp. phase 2 1.46 34.54 0.49 11.51 0.49 11.51 1.79 42.45• Total increase of links in exp1 around 40% and around 20% in exp2.That means human’s link creation behavior increased. But why?• Around 50% of links can not be explained by preceding situation(external factors?)• Around 1/3 of all links have been recommended by human and only6-12% have been recommended by botsMitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13
  26. 26. Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation between the 40% increase of sociallink creation and socialbot interaction exists.• But we observed few new links which are likely to be caused bysocialbots though the short experimental period and the cold startproblem of social bots.• Socialbots may indeed manipulate the social graph of OSN but theyare not yet as powerful as human.• Our results also highlight the role of external factors in link creation,which is partly in line with Backstrom et al. [1] and Rowe et al. [4].Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 13 / 13
  27. 27. Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation between the 40% increase of sociallink creation and socialbot interaction exists.• But we observed few new links which are likely to be caused bysocialbots though the short experimental period and the cold startproblem of social bots.• Socialbots may indeed manipulate the social graph of OSN but theyare not yet as powerful as human.• Our results also highlight the role of external factors in link creation,which is partly in line with Backstrom et al. [1] and Rowe et al. [4].Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 13 / 13
  28. 28. Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation between the 40% increase of sociallink creation and socialbot interaction exists.• But we observed few new links which are likely to be caused bysocialbots though the short experimental period and the cold startproblem of social bots.• Socialbots may indeed manipulate the social graph of OSN but theyare not yet as powerful as human.• Our results also highlight the role of external factors in link creation,which is partly in line with Backstrom et al. [1] and Rowe et al. [4].Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 13 / 13
  29. 29. Introduction Socialbot Challenge Success Measures Results ConclusionsConclusions• It is unlikely that a causal relation between the 40% increase of sociallink creation and socialbot interaction exists.• But we observed few new links which are likely to be caused bysocialbots though the short experimental period and the cold startproblem of social bots.• Socialbots may indeed manipulate the social graph of OSN but theyare not yet as powerful as human.• Our results also highlight the role of external factors in link creation,which is partly in line with Backstrom et al. [1] and Rowe et al. [4].Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 13 / 13
  30. 30. Introduction Socialbot Challenge Success Measures Results Conclusions[1] Lars Backstrom and Jure Leskovec. “Supervised random walks: predicting andrecommending links in social networks”. In:Proceedings of the fourth ACM international conference on Web search and data mining.WSDM ’11. Hong Kong, China: ACM, 2011, pp. 635–644. doi:10.1145/1935826.1935914 (cit. on pp. 26–29).[2] Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu. “Thesocialbot network: when bots socialize for fame and money”. In:Proceedings of the 27th Annual Computer Security Applications Conference. ACSAC ’11.Orlando, Florida: ACM, 2011, pp. 93–102. doi: 10.1145/2076732.2076746 (cit. on p. 2).[3] Max Nanis, Ian Pearce, and Tim Hwang. PacSocial: Field Test Report.http://www.pacsocial.com. 2011 (cit. on p. 5).[4] Matthew Rowe, Milan Stankovic, and Harith Alani. “Who will follow whom? Exploitingsemantics for link prediction in attention-information networks”. In:11th International Semantic Web Conference (ISWC 2012). 2012 (cit. on pp. 26–29).Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 13 / 13

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