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Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks

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Book cover Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 813))

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Abstract

The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the conversational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes.

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References

  1. Lazer, D., et al.: The science of fake news. Science 359, 1094–1096 (2018)

    Google Scholar 

  2. Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)

    Article  Google Scholar 

  3. Subrahmanian, V., et al.: The DARPA Twitter bot challenge. Computer 49(6), 38–46 (2016)

    Google Scholar 

  4. Varol, O., Ferrara, E., Davis, C., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: AAAI Web and Social Media, pp. 280–289 (2017)

    Google Scholar 

  5. Chavoshi, N., Hamooni, H., Mueen, A.: DeBot: Twitter bot detection via warped correlation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 817–822 (2016)

    Google Scholar 

  6. Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of Twitter accounts: are you a human, bot, or cyborg? IEEE Secur. Comput. 9(6), 811–824 (2012)

    Google Scholar 

  7. Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: BotOrNot: a system to evaluate social bots, pp. 273–274 (2016)

    Google Scholar 

  8. Stukal, D., Sanovich, S., Bonneau, R., Tucker, J.A.: Detecting bots on Russian political Twitter. Big Data 5, 310–324 (2017)

    Article  Google Scholar 

  9. Abokhodair, N., Yoo, D., McDonald, D.W.: Dissecting a social botnet: growth, content and influence in Twitter. CSCW 2016, 839–851 (2015)

    Google Scholar 

  10. Kušen, E., Strembeck, M.: Why so emotional? An analysis of emotional bot-generated content on Twitter. COMPLEXIS 2018, 13–22 (2018)

    Google Scholar 

  11. Hegelich, S., Janetzko, D.: Are social bots on Twitter political actors? Empirical evidence from a Ukrainian social botnet. In: ICWSM, pp. 579–582 (2016)

    Google Scholar 

  12. Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21(11) (2016)

    Google Scholar 

  13. Forelle, M., Howard, P.N., Monroy-Hernández, A., Savage, S.: Political bots and the manipulation of public opinion in Venezuela. In: SSRN, pp. 1–8 (2015)

    Google Scholar 

  14. Howard, P.N., Kollanyi, B.: Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum. SSRN (2016). URL: https://ssrn.com/abstract=2798311

  15. Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: Design and analysis of a social botnet. Comput. Netw. 57, 556–578 (2013)

    Article  Google Scholar 

  16. Howard, P.N., Woolley, S., Calo, R.: Algorithms, bots, and political communication in the US 2016 election. J. Inf. Technol. Polit. 15, 81–93 (2018)

    Article  Google Scholar 

  17. Zhdanova, M., Orlova, D.: Computational Propaganda in Ukraine: Caught Between External Threats and Internal Challenges. COMPROP, Oxford, UK. Working Paper (2017)

    Google Scholar 

  18. Chavoshi, N., Hamooni, H., Mueen, A.: Temporal patterns in bot activities. In: 26th International Conference on World Wide Web, pp. 1601–1606 (2017)

    Google Scholar 

  19. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    Google Scholar 

  20. Bonacich, P.: Some unique properties of eigenvector centrality. Soc. Netw. 29(4), 555–564 (2007)

    Article  Google Scholar 

  21. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)

    Article  Google Scholar 

  22. Valente, T.W., Coronges, K., Lakon, C., Costenbader, E.: How correlated are network centrality measures? Connections 28(1), 16–26 (2008)

    Google Scholar 

  23. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  24. Blondel, V.D., Guillaume, J-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008(10) (2008)

    Google Scholar 

  25. Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 10–17 (2010)

    Google Scholar 

  26. Tufekci, Z.: Big questions for social media big data: representativeness, validity and other methodological pitfalls. In 8th International Conference on Weblogs and Social Media (ICWSM 2014), pp. 505–514 (2014)

    Google Scholar 

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Acknowledgments

Special thanks to Nikan Chavoshi from New Mexico State University for support and access to DeBot. This research was partially supported through funding from the Seth Bonder Foundation.

Author information

Authors and Affiliations

  1. Computational Social Science Program, Department of Computational and Data Sciences, George Mason University, Fairfax, VA, 22030, USA

    Ross Schuchard & Andrew Crooks

  2. Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, 22030, USA

    Andrew Crooks, Anthony Stefanidis & Arie Croitoru

  3. Criminal Investigations and Network Analysis Center, George Mason University, Fairfax, VA, 22030, USA

    Anthony Stefanidis

Authors
  1. Ross Schuchard
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  2. Andrew Crooks
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  3. Anthony Stefanidis
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  4. Arie Croitoru
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Corresponding author

Correspondence to Ross Schuchard .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Schuchard, R., Crooks, A., Stefanidis, A., Croitoru, A. (2019). Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_34

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