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

Detecting deception in interpersonal communication is a pivotal issue in social psy- chology, with signi cant implications for court and criminal proceedings. In this study, four experiments were designed to compare the performance of natural lan- guage 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 signi cantly 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 sug- gests that NLP algorithms, due to their ability to handle complex patterns of linguistic data, might be useful for better disentangling truthful from deceptive narratives, out- performing traditional theoretical models.