@inproceedings{ibraheem-etal-2022-putting,
title = "Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia",
author = "Ibraheem, Samee and
Zhou, Gaoyue and
DeNero, John",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.11",
doi = "10.18653/v1/2022.naacl-main.11",
pages = "158--168",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
%A Ibraheem, Samee
%A Zhou, Gaoyue
%A DeNero, John
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F ibraheem-etal-2022-putting
%X 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.
%R 10.18653/v1/2022.naacl-main.11
%U https://aclanthology.org/2022.naacl-main.11
%U https://doi.org/10.18653/v1/2022.naacl-main.11
%P 158-168
Markdown (Informal)
[Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia](https://aclanthology.org/2022.naacl-main.11) (Ibraheem et al., NAACL 2022)
ACL