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Since the 2016 US Presidential election, social media abuse has beeneliciting massive concern in the academic community and beyond. Preventing andlimiting the malicious activity of users, such as trolls and bots, in theirmanipulation campaigns is of paramount importance for the integrity ofdemocracy, public health, and more. However, the automated detection of trollaccounts is an open challenge. In this work, we propose an approach based onInverse Reinforcement Learning (IRL) to capture troll behavior and identifytroll accounts. We employ IRL to infer a set of online incentives that maysteer user behavior, which in turn highlights behavioral differences betweentroll and non-troll accounts, enabling their accurate classification. As astudy case, we consider the troll accounts identified by the US Congress duringthe investigation of Russian meddling in the 2016 US Presidential election. Wereport promising results: the IRL-based approach is able to accurately detecttroll accounts (AUC=89.1%). The differences in the predictive features betweenthe two classes of accounts enables a principled understanding of thedistinctive behaviors reflecting the incentives trolls and non-trolls respondto.

Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election