Skip to main content

Advertisement

Log in

An empirical study of socialbot infiltration strategies in the Twitter social network

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Online social networks (OSNs) such as Twitter and Facebook have become a significant testing ground for Artificial Intelligence developers who build programs, known as socialbots, that imitate human users by automating their social network activities such as forming social links and posting content. Particularly, Twitter users have shown difficulties in distinguishing these socialbots from the human users in their social graphs. Frequently, socialbots are effective in acquiring human users as followers and exercising influence within them. While the success of socialbots is certainly a remarkable achievement for AI practitioners, their proliferation in the Twitter sphere opens many possibilities for cybercrime. The proliferation of socialbots in Twitter motivates us to assess the characteristics or strategies that make socialbots most likely to succeed. In this direction, we created 120 socialbot accounts in Twitter, which have a profile, follow other users, and generate tweets either by reposting others’ tweets or by generating their own synthetic tweets. Then, we employ a \(2^k\) factorial design experiment to quantify the infiltration performance of different socialbot strategies, and examine the effectiveness of individual profile and activity-related attributes of the socialbots. Our analysis is the first of a kind, and reveals what strategies make socialbots successful in the Twitter sphere.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • 46 % of Twitter users have less than 100 followers-Simplify360 (2014) http://simplify360.com/blog/46-of-twitter-users-have-less-than-100-followers/. Accessed 1 May 2014

  • Ahmad MA, Ahmed I, Srivastava J, Poole MS (2011) Trust me, i’m an expert: trust, homophily and expertise in mmos. In: International conference on privacy, security, risk and trust (passat) and international conference on social computing (socialcom), pp 882–887

  • Aiello LM, Deplano M, Schifanella R, Ruffo G (2012) People are strange when you’re a stranger: impact and influence of bots on social networks. In: Proceedings of AAAI international conference on web and social media (ICWSM)

  • Barbieri G, Pachet F, Roy P, Esposti MD (2012) Markov constraints for generating lyrics with style. In: Proceedings of European conference on artificial intelligence

  • Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on twitter. In: Proceedings of annual collaboration, electronic messaging, anti-abuse and spam conference (CEAS)

  • Boshmaf Y, Muslukhov I, Beznosov K, Ripeanu M (2011) The socialbot network: when bots socialize for fame and money. In: Proceedings of annual computer security applications conference (ACSAC)

  • Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in twitter: the million follower fallacy. In: Proceedings of AAAI international conference on web and social media (ICWSM)

  • Chandra J, Scholtes I, Ganguly N, Schweitzer F (2012) A tunable mechanism for identifying trusted nodes in large scale distributed networks. In: Proceedings of IEEE international conference on trust, security and privacy in computing and communications (TRUSTCOM), pp 722–729

  • Chhabra S, Aggarwal A, Benevenuto F, Kumaraguru P (2011) Phi.sh/SoCiaL: The phishing landscape through short URLs. In: Proceedings of collaboration, electronic messaging, anti-abuse and spam conference (CEAS)

  • Chu Z, Gianvecchio S, Wang H, Jajodia S (2012) Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans Dependable Secure Comput 9(6):811–824

    Article  Google Scholar 

  • Coburn Z, Marra G (2008) Realboy: believable twitter Bots. http://ca.olin.edu/2008/realboy/. Accessed 1 Dec 2015

  • Creating a bot on wikipedia (2015) http://en.wikipedia.org/wiki/Wikipedia:Creating_a_bot. Accessed 1 Dec 2015

  • Edwards J (2013) There are 20 million fake users on twitter, and twitter can’t do much about them—business insider. http://tinyurl.com/twitter-20M-fake-users. Accessed 1 Dec 2013

  • Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2014) The rise of social bots. arXiv:1407.5225

  • Freitas C, Benevenuto F, Ghosh S, Veloso A (2015) Reverse engineering socialbot infiltration strategies in twitter. In: Proceedings of ACM/IEEE international conference on advances in social networks analysis and mining (ASONAM)

  • Ghosh S, Viswanath B, Kooti F, Sharma NK, Korlam G, Benevenuto F, Gummadi, K. P. (2012) Understanding and combating link farming in the twitter social network. In: Proceedings of World Wide Web Conference (WWW)

  • Ghosh S, Zafar MB, Bhattacharya P, Sharma N, Ganguly N, Gummadi K (2013) On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream. In: Proceedings of ACM conference on information knowledge management (CIKM)

  • Gyöngyi Z, Garcia-Molina H (2005) Link spam alliances. In: Proceedings of international conference on very large data bases (VLDB)

  • Jain R (1991) The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. Wiley, London

    MATH  Google Scholar 

  • Jurafsky D, Martin JH (2000) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 1st edn. Prentice Hall PTR, Englewood Cliffs

    Google Scholar 

  • Klout—The standard for influence (2015) http://klout.com/. Accessed 1 Dec 2015

  • Klout—wikipedia (2015) http://en.wikipedia.org/wiki/Klout. Accessed 1 Dec 2015

  • Kouloumpis E, Wilson T, Moore J (2011) Twitter sentiment analysis: the good, the bad and the OMG! In: Proceedings of AAAI international conference on web and social media (ICWSM)

  • Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots + machine learning. In: Proceedings ACM SIGIR conference on research and development in information retrieval (SIGIR)

  • Lee K, Eoff BD, Caverlee J (2011) Seven months with the devils: a long-term study of content polluters on twitter. In: Proceedings of AAAI international conference on web and social media (ICWSM)

  • Let’s make candidates pledge not to use bots (2014) http://blogs.reuters.com/great-debate/2014/01/02/lets-make-candidates-pledge-not-to-use-bots/. Accessed 1 Dec 2015

  • Messias J, Schmidt L, Rabelo R, Benevenuto F (2013) You followed my bot! Transforming robots into influential users in Twitter. First Monday 18(7) http://firstmonday.org/ojs/index.php/fm/article/view/4217

  • Orcutt M (2012) Twitter mischief plagues Mexico’s election. http://www.technologyreview.com/news/428286/twitter-mischief-plagues-mexicos-election/. Accessed 1 Dec 2015

  • Pandora Bots (2015) http://www.pandorabots.com/. Accessed 1 Dec 2015

  • Pitsillidis A, Levchenko K, Kreibich C, Kanich C, Voelker GM, Paxson, V, Weaver N, Savage S (2010) Botnet judo: fighting spam with itself. In: Proceedings of symposium on network and distributed system security (NDSS), San Diego, CA

  • Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011) Truthy: mapping the spread of astroturf in microblog streams . In: Proceedings of World Wide Web Conference (WWW)

  • Roy A, Ahmad MA, Sarkar C, Keegan B, Srivastava J (2012) The ones that got away: false negative estimation based approaches for gold farmer detection. In: International conference on privacy, security, empirical study of socialbots in twitter 27 risk and trust (passat) and international conference on social computing (socialcom), pp 328–337)

  • Shane S, Hubbard B (2014) ISIS displaying a deft command of varied media. http://www.nytimes.com/2014/08/31/world/middleeast/isis-displaying-a-deft-command-of-varied-media.html. Accessed 1 Dec 2015

  • Shutting down spammers (2012) https://blog.twitter.com/2012/shutting-down-spammers. Accessed 1 Dec 2015

  • Stone-Gross B, Holz T, Stringhini G, Vigna G (2011) The underground economy of spam: a Botmaster’s perspective of coordinating large-scale spam campaigns. In: Proceedings of USENIX conference on large- scale exploits and emergent threats (LEET)

  • Subrahmanian VS, Azaria A, Durst S, Kagan V, Galstyan A, Lerman K, Waltzman R et al. (2016) The DARPA twitter bot challenge. arXiv:1601.05140

  • The twitter rules—twitter help center (2015) https://support.twitter.com/articles/18311#. Accessed 1 Dec 2015

  • Viswanath B, Post A, Gummadi KP, Mislove A (2010a) An analysis of social network-based Sybil defenses. ACM SIGCOMM Comput Commun Rev 40(4):363–374

    Article  Google Scholar 

  • Viswanath B, Post A, Gummadi KP, Mislove A (2010b) An analysis of social network-based sybil defenses. SIGCOMM Comput Commun Rev 40(4):363–374

    Article  Google Scholar 

  • Wagner C, Liao V, Pirolli P, Nelson L, Strohmaier M (2012) It’s not in their tweets: modeling topical expertise of twitter users. In: Proceedings of AASE/IEEE international conference on social computing (SocialCom)

  • Wagner C, Mitter S, Körner C, Strohmaier M (2012) When social bots attack: modeling susceptibility of users in online social networks. In: Proceedings of workshop on making sense of microposts (with WWW)

  • Wald R, Khoshgoftaar TM, Napolitano A, Sumner C (2013) Which users reply to and interact with twitter social bots? In: Proceedings of IEEE conference on tools with artificial intelligence (ICTAI)

  • Web Ecology Project (2015) http://www.webecologyproject.org/

  • William R, Avison JDM (eds) BAP (2007) Mental health, social mirror. Springer, Berlin

Download references

Acknowledgments

This work was partially supported by the project FAPEMIG-PRONEX-MASWeb, Models, Algorithms and Systems for the Web, process number APQ-01400-14, and individual grants from CNPq, CAPES, and Fapemig.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saptarshi Ghosh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Freitas, C., Benevenuto, F., Veloso, A. et al. An empirical study of socialbot infiltration strategies in the Twitter social network. Soc. Netw. Anal. Min. 6, 23 (2016). https://doi.org/10.1007/s13278-016-0331-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-016-0331-3

Keywords

Navigation