A persistent accuracy ceiling in automated verbal deception detection
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Computer Science > Computation and Language
arXiv:2610.12118 (cs)
[Submitted on 8 Oct 2026]
Title:A persistent accuracy ceiling in automated verbal deception detection
View a PDF of the paper titled A persistent accuracy ceiling in automated verbal deception detection, by Riccardo Loconte and 3 other authors
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Abstract:Automated methods have been proposed to overcome the limitations of human verbal deception detection, but evidence remains fragmented across disciplines. We systematically reviewed 25 years of research (289 reports, 6,136 classification models) and meta-analyzed 3,653 models nested within 97 datasets. Pooled accuracy was 74.4% (95% CI: 71.2%-77.4%) with substantial heterogeneity. Accuracy was driven by methodological quality (ground truth, data source, class balance, evaluation procedure) more than by model complexity: the adoption of embeddings and large language models has not translated into improved predictive performance. Only 12.46% of reports used data with verifiable ground-truth, and only 23.96% of models were evaluated on independent data. The pooled accuracy aligns with meta-analyses of manual approaches, suggesting a ceiling of 70-75%, unlikely to be lifted by current research conventions.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12118 [cs.CL] |
| (or arXiv:2610.12118v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12118
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled A persistent accuracy ceiling in automated verbal deception detection, by Riccardo Loconte and 3 other authors
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