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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Web of Science)

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
Pages (from-to)354-366
Number of pages13
JournalApplied Cognitive Psychology
Volume32
Issue number3
DOIs
Publication statusPublished - May 2018

Keywords

  • credibility assessment
  • intentions
  • machine learning
  • model statement
  • verbal deception detection
  • EPISODIC FUTURE THOUGHT
  • ELICIT INFORMATION
  • FALSE INTENTIONS
  • TRUE
  • DECEPTION
  • CUES
  • METAANALYSIS
  • INTERPRETER
  • ACCURACY
  • MARKERS

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