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        <title>Linguistic Cues to Deception and Perceived Deception in Interview Dialogues</title>
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    <abstract>We explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, identifying characteristics of statements that are perceived as truthful or deceptive by interviewers. Our analysis show significant differences between truthful and deceptive question responses, as well as variations in deception patterns across gender and native language. This analysis motivated our selection of features for machine learning experiments aimed at classifying globally deceptive speech. Our best classification performance is 72.74% F1-Score (about 17% better than human performance), which is achieved using a combination of linguistic features and individual traits.</abstract>
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%0 Conference Proceedings
%T Linguistic Cues to Deception and Perceived Deception in Interview Dialogues
%A Levitan, Sarah Ita
%A Maredia, Angel
%A Hirschberg, Julia
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F levitan-etal-2018-linguistic
%X We explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, identifying characteristics of statements that are perceived as truthful or deceptive by interviewers. Our analysis show significant differences between truthful and deceptive question responses, as well as variations in deception patterns across gender and native language. This analysis motivated our selection of features for machine learning experiments aimed at classifying globally deceptive speech. Our best classification performance is 72.74% F1-Score (about 17% better than human performance), which is achieved using a combination of linguistic features and individual traits.
%R 10.18653/v1/N18-1176
%U https://aclanthology.org/N18-1176
%U https://doi.org/10.18653/v1/N18-1176
%P 1941-1950

Markdown (Informal)

[Linguistic Cues to Deception and Perceived Deception in Interview Dialogues](https://aclanthology.org/N18-1176) (Levitan et al., NAACL 2018)

• Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (Levitan et al., NAACL 2018)

ACL

• Sarah Ita Levitan, Angel Maredia, and Julia Hirschberg. 2018. Linguistic Cues to Deception and Perceived Deception in Interview Dialogues. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1941–1950, New Orleans, Louisiana. Association for Computational Linguistics.