Introduction Socialbot Challenge Success Measures Results Conclusions

Understanding The Impact Of Socialbot Attacks InOnline Social Networks

Silvia Mitter, Claudia Wagner, Markus Strohmaier

Knowledge Technologies InstituteGraz University of Technology

ACM Web Science

May 4, 2013

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 1 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Introduction

What 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 being

The 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Research Question

To 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Research Question

Can socialbots animate previously unconnected users to connect?

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 4 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

The Experiment: Socialbot Challenge

Socialbot 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Dataset

• 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 phase

Socialbots 9Targets 2,700Number of Tweets 1,006,351

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Introduction Socialbot Challenge Success Measures Results Conclusions

Control and Experimental Phases

19.09 22.10. 12.11.control phase33 days

experimental phase 121 days

control phase33 days19.09 24.11.

experimental phase 233 days22.10.

PacSocial Exp

Modified Exp

Oct 26 2011 Nov 02 2011 Nov 09 2011 Nov 16 2011 Nov 23 20110

100

200

300

400

500

Tw

eet

Count

Tweets authored by bots

Figure: Tweets authored by bots over time.

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 7 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Measuring the Success of Socialbots

PacSocial 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 variables

Success 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Some 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 Tweet

User A User B

Mediator

starts following

creates Tweet

includesinclu

des

Figure: RT 1

Figure: RT 2 Figure: RT 3

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Some 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 Tweet

User A User B

Mediator

starts following

creates Tweet

includesinclu

des

Figure: RT 1

User A User B

Mediator

follo

ws

starts following

follows

Figure: RT 2

Figure: RT 3

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Some 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 Tweet

User A User B

Mediator

starts following

creates Tweet

includesinclu

des

Figure: RT 1

User A User B

Mediator

follo

ws

starts following

follows

Figure: RT 2

User A User B

Mediator

follo

ws

starts following

includes

creates Tweet

Figure: RT 3

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 9 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Who 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Who 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Who 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Who 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Who 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Link Creation

Link Creation ctr exp1 exp2

Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55

Basis for Calculations 3.36 4.91 4.21

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Link Creation

Link Creation ctr exp1 exp2

Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55

Basis for Calculations 3.36 4.91 4.21

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Link Creation

Link Creation ctr exp1 exp2

Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55

Basis for Calculations 3.36 4.91 4.21

follows

User A User Bstarts following

User A User Bstarts following

communicate

(a)

(b)

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Link Creation

Link Creation ctr exp1 exp2

Total 5.49 7.62 6.76-Direct User Interaction -2.12 -2.71 -2.55

Basis for Calculations 3.36 4.91 4.21

follows

User A User Bstarts following

User A User Bstarts following

communicate

(a)

(b)

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 11 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Results

Link Creation RT 123 humanmediated

socialbotmediated

human &socialbotmediated

undefinedmediated

abs % 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 bots

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Results

Link Creation RT 123 humanmediated

socialbotmediated

human &socialbotmediated

undefinedmediated

abs % 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 bots

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Results

Link Creation RT 123 humanmediated

socialbotmediated

human &socialbotmediated

undefinedmediated

abs % 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 bots

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Results

Link Creation RT 123 humanmediated

socialbotmediated

human &socialbotmediated

undefinedmediated

abs % 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 bots

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Results

Link Creation RT 123 humanmediated

socialbotmediated

human &socialbotmediated

undefinedmediated

abs % 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 bots

Mitter, Wagner, Strohmaier (TU Graz) Impact of Socialbots in OSNs May 4, 2013 12 / 13

Introduction Socialbot Challenge Success Measures Results Conclusions

Conclusions

• 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Conclusions

• 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Conclusions

• 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

Introduction Socialbot Challenge Success Measures Results Conclusions

Conclusions

• 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

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