Characterization and Comparison of Russian and Chinese Disinformation Campaigns

  • David M. BeskowEmail author
  • Kathleen M. Carley
Part of the Lecture Notes in Social Networks book series (LNSN)


While substantial research has focused on social bot classification, less computational effort has focused on repeatable bot characterization. Binary classification into “bot” or “not bot” is just the first step in social cybersecurity workflows. Characterizing the malicious actors is the next step. To that end, this paper will characterize data associated with state sponsored manipulation by Russia and the People’s Republic of China. The data studied here was associated with information manipulation by state actors, the accounts were suspended by Twitter and subsequently all associated data was released to the public. Of the multiple data sets that Twitter released, we will focus on the data associated with the Russian Internet Research Agency and the People’s Republic of China. The goal of this paper is to compare and contrast these two important data sets while simultaneously developing repeatable workflows to characterize information operations for social cybersecurity.


Bot characterization Social cybersecurity Disinformation Information operations Strategic competition Propaganada Exploratory data analysis Internet memes 



This work was supported in part by the Office of Naval Research (ONR) Award N00014182106 and Award N000141812108, and the Center for Computational Analysis of Social and Organization Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government.


  1. 1.
    Alvarez, P., Hosking, T.: The full text of Mueller’s indictment of 13 Russians’. The Atlantic, 16th Feb (2018)Google Scholar
  2. 2.
    Badawy, A., Addawood, A., Lerman, K., Ferrara, E.: Characterizing the 2016 Russian IRA influence campaign. Soc. Netw. Anal. Min. 9(1), 31 (2019)CrossRefGoogle Scholar
  3. 3.
    Beskow, D., Carley, K.M.: Bot conversations are different: leveraging network metrics for bot detection in twitter. In: Advances in Social Networks Analysis and Mining (ASONAM), 2018 International Conference on, pp. 176–183. IEEE (2018)Google Scholar
  4. 4.
    Beskow, D., Carley, K.M.: Introducing bothunter: a tiered approach to detection and characterizing automated activity on twitter. In: Bisgin, H., Hyder, A., Dancy, C., Thomson, R. (eds.) International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Springer (2018)Google Scholar
  5. 5.
    Beskow, D.M., Carley, K.M.: Army must regain initiative in social cyberwar. Army Mag. 69(8), 24–28 (2019)Google Scholar
  6. 6.
    Beskow, D.M., Carley, K.M.: Social cybersecurity: an emerging national security requirement. Mil. Rev. 99(2), 117 (2019)Google Scholar
  7. 7.
    Bowles, N.: The mainstreaming of political memes online. New York Times (Feb 2018).
  8. 8.
    Chavoshi, N., Hamooni, H., Mueen, A.: Debot: twitter bot detection via warped correlation. In: ICDM, pp. 817–822 (2016)Google Scholar
  9. 9.
    Chen, A.: The agency. N. Y. Times 2(6), 2015 (2015)Google Scholar
  10. 10.
    Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: Botornot: a system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273–274. International World Wide Web Conferences Steering Committee (2016)Google Scholar
  11. 11.
    Dawkins, R.: The Selfish Gene: With a New Introduction by the Author. Oxford University Press, Oxford (2006). (Originally published in 1976)Google Scholar
  12. 12.
    DiResta, R., Shaffer, K., Ruppel, B., Sullivan, D., Matney, R., Fox, R., Albright, J., Johnson, B.: The Tactics & Tropes of the Internet Research Agency. New Knowledge, New York (2018)Google Scholar
  13. 13.
    Faris, R., Villeneuve, N.: Measuring global internet filtering. In: Access Denied: The Practice and Policy of Global Internet Filtering, vol. 5. MIT Press, Cambridge (2008)Google Scholar
  14. 14.
    Howard, P.N., Ganesh, B., Liotsiou, D., Kelly, J., François, C.: The IRA, Social Media and Political Polarization in the United States, 2012–2018. University of Oxford, Oxford (2018)Google Scholar
  15. 15.
    McDonell, S.: Why China censors banned Winnie the pooh – BBC news. (July 2017). Accessed 29 Sep 2019
  16. 16.
    Mueller, R.S.: Report on the Investigation into Russian Interference in the 2016 Presidential Election. US Department of Justice, Washington (2019)Google Scholar
  17. 17.
    Nimmo, B., Brookie, G., Karan, K.: #trolltracker: twitter troll farm archives – DFRLAB – medium. https://file:///Users/dbeskow/Dropbox/CMU/bot_labels/references/ira/%23TrollTracker_%20Twitter%20Troll%20Farm%20Archives%20-%20DFRLab%20-%20Medium.html. Accessed 23 Sep 2019Google Scholar
  18. 18.
    Safety, T.: Information operations directed at Hong Kong. (August 2019). Accessed 26 Sep 2019
  19. 19.
    National Academies of Sciences, Engineering, and Medicine: A Decadal Survey of the Social and Behavioral Sciences: A Research Agenda for Advancing Intelligence Analysis. The National Academies Press, Washington (2019).,
  20. 20.
    Shifman, L.: The cultural logic of photo-based meme genres. J. Vis. Cult. 13(3), 340–358 (2014)CrossRefGoogle Scholar
  21. 21.
    Shifman, L.: Memes in digital culture. MIT Press, London (2014)Google Scholar
  22. 22.
    Uren, T., Thomas, E., Wallis, J.: Tweeting Through the Great Firewall: Preliminary Analysis of PRC-linked Information Operations on the Hong Kong Portest. Australia Strategic Policy Institute: International Cyber Policy Center, Barton (2019)Google Scholar
  23. 23.
    Von Nordheim, G., Boczek, K., Koppers, L.: Sourcing the sources: An analysis of the use of twitter and facebook as a journalistic source over 10 years in the New York times, the guardian, and süddeutsche zeitung. Digit. Journal. 6(7), 807–828 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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