Analyzing the Strategy of Propaganda using Inverse Reinforcement
  Learning: Evidence from the 2022 Russian Invasion of Ukraine
Dominique Geissler Stefan Feuerriegel
The 2022 Russian invasion of Ukraine was accompanied by a large-scale,
pro-Russian propaganda campaign on social media. However, the strategy behind
the dissemination of propaganda has remained unclear, particularly how the
online discourse was strategically shaped by the propagandists' community.
Here, we analyze the strategy of the Twitter community using an inverse
reinforcement learning (IRL) approach. Specifically, IRL allows us to model
online behavior as a Markov decision process, where the goal is to infer the
underlying reward structure that guides propagandists when interacting with
users with a supporting or opposing stance toward the invasion. Thereby, we aim
to understand empirically whether and how between-user interactions are
strategically used to promote the proliferation of Russian propaganda. For
this, we leverage a large-scale dataset with 349,455 posts with pro-Russian
propaganda from 132,131 users. We show that bots and humans follow a different
strategy: bots respond predominantly to pro-invasion messages, suggesting that
they seek to drive virality; while messages indicating opposition primarily
elicit responses from humans, suggesting that they tend to engage in critical
discussions. To the best of our knowledge, this is the first study analyzing
the strategy behind propaganda from the 2022 Russian invasion of Ukraine
through the lens of IRL.