Research output per year
Research output per year
Bennett Kleinberg*, Yaloe van der Toolen, Aldert Vrij, Arnoud Arntz, Bruno Verschuere
Research output: Contribution to journal › Article › Academic › peer-review
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 language | English |
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Pages (from-to) | 354-366 |
Number of pages | 13 |
Journal | Applied Cognitive Psychology |
Volume | 32 |
Issue number | 3 |
DOIs | |
Publication status | Published - May 2018 |
Research output: Contribution to journal › Erratum / corrigendum / retractions › Academic