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While neural networks demonstrate a remarkable ability to model linguisticcontent, capturing contextual information related to a speaker's conversationalrole is an open area of research. In this work, we analyze the effect ofspeaker role on language use through the game of Mafia, in which participantsare assigned either an honest or a deceptive role. In addition to building aframework to collect a dataset of Mafia game records, we demonstrate that thereare differences in the language produced by players with different roles. Weconfirm that classification models are able to rank deceptive players as moresuspicious than honest ones based only on their use of language. Furthermore,we show that training models on two auxiliary tasks outperforms a standardBERT-based text classification approach. We also present methods for using ourtrained models to identify features that distinguish between player roles,which could be used to assist players during the Mafia game.

Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia