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Computational Measures of Deceptive Language: Prospects and Issues
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In: ISSN: 2297-900X ; EISSN: 2297-900X ; Frontiers in Communication ; https://hal.archives-ouvertes.fr/hal-03629780 ; Frontiers in Communication, Frontiers, 2022, 7, pp.792378. ⟨10.3389/fcomm.2022.792378⟩ (2022)
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A corpus-based investigation into verbal cues to deception and their sociolinguistic distribution ...
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Editors’ Review and Introduction: Lying in Logic, Language, and Cognition
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In: ISSN: 1756-8757 ; EISSN: 1756-8765 ; Topics in cognitive science ; https://hal.archives-ouvertes.fr/hal-03014151 ; Topics in cognitive science, Wiley, 2020, 12 (2), pp.466-484. ⟨10.1111/tops.12492⟩ (2020)
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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Deception in Spoken Dialogue: Classification and Individual Differences
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| 9 |
Determining indicators of deception in computer mediated communication using eye tracking
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In: Graduate Theses and Dissertations (2018)
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Character N-Grams for Detecting Deceptive Controversial Opinions
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A survey on author profiling, deception, and irony detection for the Arabic language
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Tracking the truth: the effect of face familiarity on eye fixations during deception
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Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation
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| 15 |
Differences in Language Used by Deceivers and Truth-Tellers in Thai Online Chat
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In: Journal of the Southeast Asian Linguistics Society, Vol 10, Iss 2, Pp 90-114 (2017) (2017)
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A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text
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Truth in text: Why simple language is perceived as more credible
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| 18 |
Identifying Universal Linguistic Features Associated with Veracity and Deception
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In: DTIC (2015)
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| 19 |
Exoneration or Observation? Examining a Novel Difference Between Liars and Truth Tellers
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In: FIU Electronic Theses and Dissertations (2015)
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Are computers effective lie detectors? : a meta-analysis of linguistic cues todeception
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In: Personality and Social Psychology Review 19(4): 307– 342, DOI:10.1177/1088868314556539 (2015)
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Abstract:
This meta-analysis investigates linguistic cues to deception and whether these cues can be detected with computer programs. We integrated operational definitions for 79 cues from 44 studies where software had been used to identify linguistic deception cues. These cues were allocated to six research questions. As expected, the meta-analyses demonstrated that, relative to truth-tellers, liars experienced greater cognitive load, expressed more negative emotions, distanced themselves more from events, expressed fewer sensory–perceptual words, and referred less often to cognitive processes. However, liars were not more uncertain than truth-tellers. These effects were moderated by event type, involvement, emotional valence, intensity of interaction, motivation, and other moderators. Although the overall effect size was small, theorydriven predictions for certain cues received support. These findings not only further our knowledge about the usefulness of linguistic cues to detect deception with computers in applied settings but also elucidate the relationship between language and deception.
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Keyword:
computer program; detection of deception; linguistic cues; meta-analysis; Psychology
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URL: http://geb.uni-giessen.de/geb/volltexte/2017/12444/
http://nbn-resolving.org/urn:nbn:de:hebis:26-opus-124445
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