Detecting Deception Through Linguistic Cues: From Reality Monitoring to Natural Language Processing

Abstract

Detecting deception in interpersonal communication is a pivotal issue in social psychology, with significant implications for court and criminal proceedings. In this study, four experiments were designed to compare the performance of natural language processing (NLP) techniques and human judges in detecting deception from linguistic cues in a dataset of 62 transcriptions of video-taped interviews (32 genuine and 30 deceptive). The results showed that machine-learning algorithms significantly outperform naïve (accuracy = 54.7%) and expert judges (accuracy = 59.4%) when trained on features from the reality monitoring (RM) and cognitive load frameworks (accuracy = 69.4%) or on features automatically extracted through NLP techniques (accuracy = 77.3%) but not when trained on the RM criteria alone. This evidence suggests that NLP algorithms, due to their ability to handle complex patterns of linguistic data, might be useful for better disentangling truthful from deceptive narratives, outperforming traditional theoretical models.