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

Samee Ibraheem, Gaoyue Zhou, John DeNero


Abstract
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.
Anthology ID:
2022.naacl-main.11
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
158–168
Language:
URL:
https://aclanthology.org/2022.naacl-main.11
DOI:
10.18653/v1/2022.naacl-main.11
Bibkey:
Cite (ACL):
Samee Ibraheem, Gaoyue Zhou, and John DeNero. 2022. Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 158–168, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia (Ibraheem et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.11.pdf
Data
The Mafia Dataset