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 27% better than human performance), which is achieved using a combination of linguistic features and individual traits.
| Original language | English |
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| Title of host publication | Long Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 1941-1950 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781948087278 |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 |
Publication series
| Name | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference |
|---|---|
| Volume | 1 |
Conference
| Conference | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 |
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| Country/Territory | United States |
| City | New Orleans |
| Period | 1/06/18 → 6/06/18 |
Bibliographical note
Publisher Copyright:© 2018 The Association for Computational Linguistics.