Reading between the turns: statistical modeling for identity recognition and verification in chats

Identity safekeeping has recently become an important problem for the social web: as a case study, we focus here on instant messaging platforms, proposing novel soft-biometric cues for user recognition and verification. Specifically, we design a set of features encoding effectively how a person converses: since chats are crossbreeds of written text and face-to-face verbal communication, the features inherit equally from textual authorship attribution and conversational analysis of speech. Importantly, our cues ignore completely the semantics of the chat, relying solely on non-verbal aspects, taking care of possible privacy and ethical issues. We apply our approach on a novel dataset of 94 different individuals, whose chat conversations have been recorded for an average period of five months; recognition rate, intended as normalized AUC on CMC curve, is 95.73%, while verification rate amounts to 95.66%, as normalized AUC on ROC curve