  Since the 2016 US Presidential election, social media abuse has been
eliciting massive concern in the academic community and beyond. Preventing and
limiting the malicious activity of users, such as trolls and bots, in their
manipulation campaigns is of paramount importance for the integrity of
democracy, public health, and more. However, the automated detection of troll
accounts is an open challenge. In this work, we propose an approach based on
Inverse Reinforcement Learning (IRL) to capture troll behavior and identify
troll accounts. We employ IRL to infer a set of online incentives that may
steer user behavior, which in turn highlights behavioral differences between
troll and non-troll accounts, enabling their accurate classification. As a
study case, we consider the troll accounts identified by the US Congress during
the investigation of Russian meddling in the 2016 US Presidential election. We
report promising results: the IRL-based approach is able to accurately detect
troll accounts (AUC=89.1%). The differences in the predictive features between
the two classes of accounts enables a principled understanding of the
distinctive behaviors reflecting the incentives trolls and non-trolls respond
to.
