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        <title>Box of Lies: Multimodal Deception Detection in Dialogues</title>
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        <namePart type="given">Felix</namePart>
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    <name type="personal">
        <namePart type="given">Rada</namePart>
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            <namePart type="given">Christy</namePart>
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    <abstract>Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.</abstract>
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%0 Conference Proceedings
%T Box of Lies: Multimodal Deception Detection in Dialogues
%A Soldner, Felix
%A Pérez-Rosas, Verónica
%A Mihalcea, Rada
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F soldner-etal-2019-box
%X Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.
%R 10.18653/v1/N19-1175
%U https://aclanthology.org/N19-1175
%U https://doi.org/10.18653/v1/N19-1175
%P 1768-1777

Markdown (Informal)

[Box of Lies: Multimodal Deception Detection in Dialogues](https://aclanthology.org/N19-1175) (Soldner et al., NAACL 2019)

• Box of Lies: Multimodal Deception Detection in Dialogues (Soldner et al., NAACL 2019)

ACL

• Felix Soldner, Verónica Pérez-Rosas, and Rada Mihalcea. 2019. Box of Lies: Multimodal Deception Detection in Dialogues. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1768–1777, Minneapolis, Minnesota. Association for Computational Linguistics.