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On-line social platforms implement moderation mechanisms to filter out unwanted content and to take action against possible cases of verbal aggression and abuse, sexual harassment, and such. In this study, the authors investigate chat biometrics, the identification of users from their verbal behaviour on a social platform. The typical application scenarios are the re-identification of banned users, returning under different identities, and aggressors operating through multiple fake accounts. They propose a novel processing pipeline, and contrast the problem with the authorship recognition problem, which is relatively well-studied in the literature. They evaluate the proposed approach on a large corpus of multiparty chat records in Turkish, which they have previously collected from a multiplayer game environment. They also introduce a new corpus in this study, collected from a well-known Turkish social platform called Ekşisözlük, in order to test the robustness of the system across domain changes, as well as on Portuguese and English news datasets to test it on different languages. They evaluate both instance-based and profile-based approaches, and provide detailed analyses with regards to the required amount of text to identify a person reliably.

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