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

  • Ross SchuchardEmail author
  • Andrew Crooks
  • Anthony Stefanidis
  • Arie Croitoru
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)


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.


Bots Online social networks Social network analysis 



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.


  1. 1.
    Lazer, D., et al.: The science of fake news. Science 359, 1094–1096 (2018)Google Scholar
  2. 2.
    Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)CrossRefGoogle Scholar
  3. 3.
    Subrahmanian, V., et al.: The DARPA Twitter bot challenge. Computer 49(6), 38–46 (2016)Google Scholar
  4. 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. 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. 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. 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. 8.
    Stukal, D., Sanovich, S., Bonneau, R., Tucker, J.A.: Detecting bots on Russian political Twitter. Big Data 5, 310–324 (2017)CrossRefGoogle Scholar
  9. 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. 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. 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. 12.
    Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21(11) (2016)Google Scholar
  13. 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. 14.
    Howard, P.N., Kollanyi, B.: Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum. SSRN (2016). URL:
  15. 15.
    Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: Design and analysis of a social botnet. Comput. Netw. 57, 556–578 (2013)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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. 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. 19.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)Google Scholar
  20. 20.
    Bonacich, P.: Some unique properties of eigenvector centrality. Soc. Netw. 29(4), 555–564 (2007)CrossRefGoogle Scholar
  21. 21.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  22. 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. 23.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  24. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computational Social Science Program, Department of Computational and Data SciencesGeorge Mason UniversityFairfaxUSA
  2. 2.Department of Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxUSA
  3. 3.Criminal Investigations and Network Analysis Center, George Mason UniversityFairfaxUSA

Personalised recommendations