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Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

Kleinberg, B; van der Toolen, Y; Vrij, A; Arntz, A; Verschuere, B; (2018) Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling. Applied Cognitive Psychology , 32 (3) pp. 354-366. 10.1002/acp.3407. Green open access

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Abstract

Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.

Type: Article
Title: Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/acp.3407
Publisher version: https://doi.org/10.1002/acp.3407
Language: English
Additional information: © 2018 The Authors Applied Cognitive Psychology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
Keywords: credibility assessment, intentions, machine learning, model statement, verbal deception detection
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10073076
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