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

Riccardo Loconte et al.

Journal of Language and Social Psychology2025https://doi.org/10.1177/0261927x251316883article
AJG 2
Weight
0.46

What the paper says

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.

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https://doi.org/https://doi.org/10.1177/0261927x251316883

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@article{riccardo2025,
  title        = {{Detecting Deception Through Linguistic Cues: From Reality Monitoring to Natural Language Processing}},
  author       = {Riccardo Loconte et al.},
  journal      = {Journal of Language and Social Psychology},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/0261927x251316883},
}

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

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.