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

Bennett Kleinberg, Yaloe Van Der Toolen, Aldert Vrij, Arnoud Arntz, Bruno Verschuere

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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.
Original languageEnglish
Number of pages13
JournalApplied Cognitive Psychology
Early online date2 Apr 2018
DOIs
Publication statusEarly online - 2 Apr 2018

Keywords

  • credibility assessment
  • intentions
  • machine learning
  • model statement
  • verbal deception detection

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