Automated Verbal Credibility Assessment of Intentions: The Model Statement Technique and Predictive Modeling

Autor/inn/en	Kleinberg, Bennett; van der Toolen, Yaloe; Vrij, Aldert; Arntz, Arnoud; Verschuere, Bruno
Titel	Automated Verbal Credibility Assessment of Intentions: The Model Statement Technique and Predictive Modeling
Quelle	In: Applied Cognitive Psychology, 32 (2018) 3, S.354-366 (13 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kleinberg, Bennett) ORCID (Vrij, Aldert)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0888-4080
DOI	10.1002/acp.3407
Schlagwörter	Credibility; Deception; Intention; Prediction; Interpersonal Communication; Language Usage + Suchen Sie Ihr Suchwort? Glaubwürdigkeit; Täuschung; Vorhersage; Interpersonale Kommunikation; Sprachgebrauch
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01