A persistent accuracy ceiling in automated verbal deception detection
cs.CL
Submitted: 2026-10-08
Updated: 2026-10-08
License: http://creativecommons.org/licenses/by/4.0/
The gist: Automated methods have been proposed to overcome the limitations of human verbal deception detection, but evidence remains fragmented across disciplines.
Terminology
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.
Sources
- Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection
- Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis
- PU-Lie: Lightweight Deception Detection in Imbalanced Diplomatic Dialogues via Positive-Unlabeled Learning
- Deception Detection in Videos
- Advancing Automated Deception Detection: A Multimodal Approach to Feature Extraction and Analysis
- Voting-based Multimodal Automatic Deception Detection
- The Mafiascum Dataset: A Large Text Corpus for Deception Detection
- What if Deception Cannot be Detected? A Cross-Linguistic Study on the Limits of Deception Detection from Text
- Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis
- Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques
- Voting for Deceptive Opinion Spam Detection
- Fraud detection in telephone conversations for financial services using linguistic features
- MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection
- Attention-based Bidirectional LSTM for Deceptive Opinion Spam Classification
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