Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on Twitter

  • Savvas Zannettou Max-Planck-Institut für Informatik
  • Tristan Caulfield University College London
  • Barry Bradlyn University of Illinois at Urbana-Champaign
  • Emiliano De Cristofaro University College London
  • Gianluca Stringhini Boston University
  • Jeremy Blackburn Binghamton University

Abstract

State-sponsored organizations are increasingly linked to efforts aimed to exploit social media for information warfare and manipulating public opinion. Typically, their activities rely on a number of social network accounts they control, aka trolls, that post and interact with other users disguised as “regular” users. These accounts often use images and memes, along with textual content, in order to increase the engagement and the credibility of their posts.

In this paper, we present the first study of images shared by state-sponsored accounts by analyzing a ground truth dataset of 1.8M images posted to Twitter by accounts controlled by the Russian Internet Research Agency. First, we analyze the content of the images as well as their posting activity. Then, using Hawkes Processes, we quantify their influence on popular Web communities like Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, with respect to the dissemination of images. We find that the extensive image posting activity of Russian trolls coincides with real-world events (e.g., the Unite the Right rally in Charlottesville), and shed light on their targets as well as the content disseminated via images. Finally, we show that the trolls were more effective in disseminating politics-related imagery than other images.

Published
2020-05-26
How to Cite
Zannettou, S., Caulfield, T., Bradlyn, B., De Cristofaro, E., Stringhini, G., & Blackburn, J. (2020). Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 774-785. Retrieved from https://aaai.org/ojs/index.php/ICWSM/article/view/7342