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