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1
Computational Measures of Deceptive Language: Prospects and Issues
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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2
A corpus-based investigation into verbal cues to deception and their sociolinguistic distribution ...
Gillings, Mathew. - : Lancaster University, 2021
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3
Deception Detection Using Machine Learning
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4
Editors’ Review and Introduction: Lying in Logic, Language, and Cognition
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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5
The Role of Linguistics in Veracity Evaluation ...
Nicklaus, Martina; Stein, Dieter. - : International Language and Law Association (ILLA), 2020
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6
Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
Fornaciari, Tommaso; Cagnina, Leticia; Rosso, Paolo. - : Springer-Verlag, 2020
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7
Deception in Spoken Dialogue: Classification and Individual Differences
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8
Deception Detection in Political Statements
Kamboj, Manvi. - 2019
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9
Determining indicators of deception in computer mediated communication using eye tracking
In: Graduate Theses and Dissertations (2018)
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10
Character N-Grams for Detecting Deceptive Controversial Opinions
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11
A survey on author profiling, deception, and irony detection for the Arabic language
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12
Deception detection in dialogues
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13
Tracking the truth: the effect of face familiarity on eye fixations during deception
Millen, Ailsa E.; Hope, Lorraine; Hillstrom, Anne P. - : Taylor and Francis, 2017
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14
Detecting Deceptive Opinions: Intra and Cross-domain Classification using an Efficient Representation
Cagnina, Leticia; Rosso, Paolo. - : World Scientific, 2017
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15
Differences in Language Used by Deceivers and Truth-Tellers in Thai Online Chat
In: Journal of the Southeast Asian Linguistics Society, Vol 10, Iss 2, Pp 90-114 (2017) (2017)
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16
A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text
Minhas, Saliha Z. - : University of Stirling, 2016
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17
Truth in text: Why simple language is perceived as more credible
Owen, Helen. - : University of Otago, 2016
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18
Identifying Universal Linguistic Features Associated with Veracity and Deception
In: DTIC (2015)
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19
Exoneration or Observation? Examining a Novel Difference Between Liars and Truth Tellers
In: FIU Electronic Theses and Dissertations (2015)
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20
Are computers effective lie detectors? : a meta-analysis of linguistic cues todeception
In: Personality and Social Psychology Review 19(4): 307– 342, DOI:10.1177/1088868314556539 (2015)
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