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

Luceri, Luca; Giordano, Silvia; Ferrara, Emilio (June 2020). Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election (In Press). In: 2020 International Conference of Web and Social Media (ICWSM 2020). Atlanta, USA. June 8-10 2020.

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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.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)
08 Faculty of Science > Institute of Computer Science (INF)

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics
600 Technology > 620 Engineering

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

17 Sep 2020 14:36

Last Modified:

17 Sep 2020 14:36

ArXiv ID:

2001.10570v3

BORIS DOI:

10.7892/boris.146563

URI:

https://boris.unibe.ch/id/eprint/146563

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