Authorship recognition in a multiparty chat scenario

Users of online social networks often use multiple identities. This paper investigates the possibility of identifying a user from his or her chat behavior in such a setting. We have collected a large corpus of multiparty chat records in Turkish, obtained from a multiplayer game database. The most active 978 users are selected according to their participation in game chat sessions. This corpus is used in a biometric identification experiment where we seek each user among a gallery of users. Character matrices for each player are used as features, and re-centered local profiles and cosine similarity measure are preferred as identification methods. We systematically assess the effect of text normalization on identification. We report comparative results, the best of which reach around 75% rank-1 accuracy for a gallery size of 978.