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Detection of Coordination Between State-Linked Actors

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13558)


Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked compared to unaffiliated accounts. We find that the state-linked actors exhibit more coordination amongst themselves than with the unaffiliated accounts. The degree of coordination between the state-linked accounts is also much higher than the observed coordination between the unaffiliated accounts. Additionally, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure.


  • Coordinated activity
  • Influence modeling
  • Markov chains

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  1. Alizadeh, M., Shapiro, J.N., Buntain, C., Tucker, J.A.: Content-based features predict social media influence operations. Sci. Adv. 6(30), eabb5824 (2020)

    Google Scholar 

  2. Asavathiratham, C.: The Influence Model: A Tractable Representation for the Dynamics of Networked Markov Chains. Ph.D. thesis, Massachusetts Institute of Technology (2001)

    Google Scholar 

  3. Asavathiratham, C., Roy, S., Lesieutre, B., Verghese, G.: The influence model. IEEE Control Syst. Mag. 21(6), 52–64 (2001)

    CrossRef  Google Scholar 

  4. Basu, S., Choudhury, T., Clarkson, B., Pentland, A.: Learning human interactions with the influence model. NIPS (2001)

    Google Scholar 

  5. Dong, W., Lepri, B., Cappelletti, A., Pentland, A., Pianesi, F., Zancanaro, M.: Using the influence model to recognize functional roles in meetings. In: Proceedings of the 9th International Conference on Multimodal Interfaces, pp. 271–278 (2007)

    Google Scholar 

  6. Erhardt, K., Pentland, A.: Disambiguating disinformation: Extending beyond the veracity of online content. Workshop Proceedings of the 15th International AAAI Conference on Web and Social Media (2021)

    Google Scholar 

  7. Facebook: Threat report the state of influence operations 2017–2020. Accessed 24 Jun 2022

  8. King, G., Pan, J., Roberts, M.E.: How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. Am. Polit. Sci. Rev. 111(3), 484–501 (2017)

    CrossRef  Google Scholar 

  9. Luceri, L., Giordano, S., Ferrara, E.: Detecting troll behavior via inverse reinforcement learning: a case study of Russian trolls in the 2016 us election. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 417–427 (2020)

    Google Scholar 

  10. Magelinski, T., Carley, K.M.: Detecting coordinated behavior in the twitter campaign to reopen America. In: Center for Informed Democracy & Social-Cybersecurity Annual Conference, IDeaS (2020)

    Google Scholar 

  11. Pan, W., Dong, W., Cebrian, M., Kim, T., Fowler, J.H., Pentland, A.: Modeling dynamical influence in human interaction: Using data to make better inferences about influence within social systems. IEEE Signal Process. Mag. 29(2), 77–86 (2012)

    CrossRef  Google Scholar 

  12. Pompeo, M.: Determination of the secretary of state on atrocities in xinjiang. Accessed 24 Jun 2022

  13. Rheault, L., Musulan, A.: Efficient detection of online communities and social bot activity during electoral campaigns. J. Inf. Technol. Politics 18(3), 324–337 (2021)

    CrossRef  Google Scholar 

  14. Starbird, K., Arif, A., Wilson, T.: Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. In: Proceedings of the ACM on Human-Computer Interaction 3(CSCW), pp. 1–26 (2019)

    Google Scholar 

  15. Twitter: Transparency report: Information operations. Last accessed 24 Jun 2022

  16. Vargas, L., Emami, P., Traynor, P.: On the detection of disinformation campaign activity with network analysis. In: Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, pp. 133–146 (2020)

    Google Scholar 

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Correspondence to Keeley Erhardt .

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Erhardt, K., Pentland, A. (2022). Detection of Coordination Between State-Linked Actors. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022. Lecture Notes in Computer Science, vol 13558. Springer, Cham.

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  • Print ISBN: 978-3-031-17113-0

  • Online ISBN: 978-3-031-17114-7

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