Linguistic Cues to Deception and Perceived Deception in Interview Dialogues

Sarah Ita Levitan, Angel Maredia, Julia Hirschberg


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.
Anthology ID:
N18-1176
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1941–1950
Language:
URL:
https://www.aclweb.org/anthology/N18-1176
DOI:
10.18653/v1/N18-1176
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1176.pdf
Video:
 http://vimeo.com/277672970