Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine
Dominique Geissler, Stefan Feuerriegel

TL;DR
This paper employs inverse reinforcement learning to analyze the strategic behavior of Twitter users and bots propagating pro-Russian propaganda during the 2022 Ukraine invasion, revealing different interaction patterns and underlying strategies.
Contribution
It introduces the first IRL-based analysis of propaganda strategies in the context of the 2022 Russian invasion of Ukraine, modeling online behavior as a Markov decision process.
Findings
Bots mainly respond to pro-invasion messages to drive virality.
Humans engage more critically with opposition messages.
Different strategies are employed by bots and humans in propagandist interactions.
Abstract
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…
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Taxonomy
TopicsMisinformation and Its Impacts · Opinion Dynamics and Social Influence · Social Media and Politics
