The rise of social bots

Communications of the ACM

Volumn 59, Issue 7, 2016, Pages 96-104

  Ferrara, Emilio ^a,b,c   Varol, Onur ^c   Davis, Clayton ^c   Menczer, Filippo ^c   Flammini, Alessandro ^c  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DESIGN; HYBRID SYSTEMS;

COMPUTING COMMUNITY; FEATURE-BASED; NETWORK-BASED; SOCIAL BOTS; SOCIAL MEDIA; TECHNO SOCIALS;

SOCIAL NETWORKING (ONLINE);

EID: 84977071823     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/2818717     Document Type: Article

References (43)

1. Abokhodair, N., Yoo, D. and McDonald, D.W. Dissecting a social botnet: Growth, content, and influence in Twitter. In Proceedings of the 18th ACM Conference on Computer-Supported Cooperative Work and Social Computing (2015). ACM.
2. Aiello, L.M., Deplano, M., Schifanella, R. and Ruffo, G. People are strange when youre a stranger: Impact and influence of bots on social networks. In Proceedings of the 6th AAAI International Conference on Weblogs and Social Media (2012). AAAI, 10-17.
3. Alvisi, L., Clement, A., Epasto, A., Lattanzi, S. and Panconesi, A. Sok: The evolution of sybil defense via social networks. In Proceedings of the 2013 IEEE Symposium on Security and Privacy. IEEE, 382-396.
4. Beutel, A., Xu, W., Guruswami, V., Palow, C. and Faloutsos, C. Copy-Catch: stopping group attacks by spotting lockstep behavior in social networks. In Proceedings of the 22nd International Conference on World Wide Web (2013), 119-130.
5. Bollen, J., Mao, H. and Zeng, X. Twitter mood predicts the stock market. J. Computational Science 2, 1 (2011), 1-8.
6. Boshmaf, Y., Muslukhov, I., Beznosov, K. and Ripeanu, M. Key challenges in defending against malicious socialbots. In Proceedings of the 5th USENIX Conference on Large-scale Exploits and Emergent Threats, Vol. 12 (2012).
7. Boshmaf, Y., Muslukhov, I., Beznosov, K. and Ripeanu, M. 2013. Design and analysis of a social botnet. Computer Networks 57, 2 (2013), 556-578.
8. Briscoe, E.J., Appling, D.S. and Hayes, H. Cues to deception in social media communications. In Proceedings of the 47th Hawaii International Conference on System Sciences (2014). IEEE, 1435-1443.
9. Cao, Q., Sirivianos, M., Yang, X. and Pregueiro, T. Aiding the detection of fake accounts in large scale social online services. NSDI (2012). 197-210.
10. Cao, Q., Yang, X., Yu, J. and Palow, C. Uncovering large groups of active malicious accounts in online social networks. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 477-488.
11. Cassa, C.A., Chunara, R., Mandl, K. and Brownstein, J.S. Twitter as a sentinel in emergency situations: Lessons from the Boston marathon explosions. PLoS Currents: Disasters (July 2013); http://dx.doi.org/10.1371/currents.dis. ad70cd1c8bc585e9470046cde334ee4b
12. Conover, M., Ratkiewicz, J., Francisco, M., Gonçalves, B., Menczer, F. and Flammini, A. Political polarization on Twitter. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (2011), 89-96.
13. Davis, C.A., Varol, O., Ferrara, E., Flammini, A. and Menczer, F. BotOrNot: A system to evaluate social bots. In Proceedings of the 25th International World Wide Web Conference Companion (2016); http://dx.doi. org/10.1145/2872518.2889302 Forthcoming. Preprint arXiv:1602.00975.
14. Edwards, C., Edwards, A., Spence, P.R. and Shelton, A.K. Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Computers in Human Behavior 33 (2014), 372-376.
15. Elovici, Y., Fire, M., Herzberg, A. and Shulman, H. Ethical considerations when employing fake identities in online social networks for research. Science and Engineering Ethics (2013), 1-17.
16. Elyashar, A., Fire, M., Kagan, D. and Elovici, Y. Homing socialbots: Intrusion on a specific organizations employee using Socialbots. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, 1358-1365.
17. Freitas, C.A. et al. Reverse engineering socialbot infiltration strategies in Twitter. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM 2015.
18. Ghosh, R., Surachawala, T. and Lerman, K. Entropybased classification of "retweeting" activity on Twitter. In Proceedings of the KDD Workshop on Social Network Analysis (2011).
19. Golder, S.A. and Macy, M.W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 6051 (2011), 1878-1881.
20. Gupta, A., Lamba, H. and Kumaraguru, P. $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter. eCrime Researchers Summit. IEEE (2013), 1-12.
21. Heymann, P., Koutrika, G. and Garcia-Molina, H. Fighting spam on social web sites: A survey of approaches and future challenges. Internet Computing 11, 6 (2007). IEEE, 36-45.
22. Hwang, T., Pearce, I. and Nanis, M. Socialbots: Voices from the fronts. ACM Interactions 19, 2 (2012), 38-45.
23. Kramer, A.D. Guillory, J.E. and Hancock, J.T. Experimental evidence of massive-scale emotional contagion through social networks. In Proceedings of the National Academy of Sciences (2014), 201320040.
24. Lee, K., Eoff, B.D., and Caverlee, J. Seven months with the devils: A long-Term study of content polluters on Twitter. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (2011), 185-192.
25. Messias, J., Schmidt, L., Oliveira, R. and Benevenuto, F. You followed my bot! Transforming robots into influential users in Twitter. First Monday 18, 7 (2013).
26. Metaxas, P.T. and Mustafaraj, E. Social media and the elections. Science 338, 6106 (2012), 472-473.
27. Paradise, A., Puzis, R. and Shabtai, A. Antireconnaissance tools: Detecting targeted socialbots. Internet Computing 18, 5 (2014), 11-19.
28. Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A. and Menczer, F. Detecting and tracking political abuse in social media. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (2011). 297-304.
29. Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A. and Menczer, F. Truthy: Mapping the spread of astroturf in microblog streams. In Proceedings of the 20th International Conference on the World Wide Web (2011), 249-252.
30. Stein, T., Chen, E. and Mangla, K. Facebook immune system. In Proceedings of the 4th Workshop on Social Network Systems (2011). ACM, 8.
31. Stringhini, G., Kruegel, C. and Vigna, G. Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference (2010), ACM, 1-9.
32. Subrahmanian, VS, Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., Menczer, F. and others. The DARPA Twitter Bot Challenge. IEEE Computer (2016). In press. Preprint arXiv:1601.05140.
33. Turing, A.M. Computing machinery and intelligence. Mind 49, 236 (1950), 433-460.
34. Viswanath, B., Post, A., Gummadi, K.P. and Mislove, A. An analysis of social network-based sybil defenses. ACM SIGCOMM Computer Communication Review 41, 4 (2011), 363-374.
35. Wagner, C., Mitter, S., Körner, S. and Strohmaier, M. When social bots attack: Modeling susceptibility of users in online social networks. In Proceedings of the 21st International Conference on World Wide Web (2012), 41-48.
36. Wald, R., Khoshgoftaar, T.M., Napolitano, A. and Sumner, C. Predicting susceptibility to social bots on Twitter. In Proceedings of the 14th IEEE International Conference on Information Reuse and Integration. IEEE, 6-13.
37. Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H. and Zhao, B.Y. You are how you click: Clickstream analysis for sybil detection. USENIX Security (2013), 241-256.
38. Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M., Zheng, H. and Zhao, B.Y. Social turing tests: Crowdsourcing sybil detection. NDSS. The Internet Society, 2013.
39. Weizenbaum, J. ELIZA-A computer program for the study of natural language communication between man and machine. Commun. ACM 9, 1 (Sept. 1966), 36-45.
40. Wu, X., Feng, Z., Fan, W., Gao, J. and Yu, Y. Detecting marionette microblog users for improved information credibility. Machine Learning and Knowledge Discovery in Databases. Springer, 2013, 483-498.
41. Xie, Y., Yu, F., Ke, Q., Abadi, M., Gillum, E., Vitaldevaria, K., Walter, J., Huang, J. and Mao, Z.M. Innocent by association: Early recognition of legitimate users. In Proceedings of the 2012 ACM Conference on Computer and Communications Security. ACM, 353-364.
42. Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B.Y. and Dai, Y. 2014. Uncovering social network sybils in the wild. ACM Trans. Knowledge Discovery from Data 8, 1 (2014), 2.
43. Zangerle, E. and Specht, G. Sorry, I was hacked A classification of compromised Twitter accounts. In Proceedings of the 29th Symposium On Applied Computing (2014).