# UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection

arXiv: https://arxiv.org/abs/2306.02827 · HTML: https://mlsanity.com/paper/2306.02827/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [The Mafiascum Dataset: A Large Text Corpus for Deception Detection](https://mlsanity.com/paper/1811.07851/) (2018)
- [Corpus Similarity Measures Remain Robust Across Diverse Languages](https://mlsanity.com/paper/2206.04332/) (2022)
- [Online Deception Detection Refueled by Real World Data Collection](https://mlsanity.com/paper/1707.09406/) (2017)

## Built on by (most cited first, 3 of 3)

- [Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception](https://mlsanity.com/paper/2403.10185/) (2024)
- [The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning](https://mlsanity.com/paper/2605.17113/) (2026)
- [How Entangled is Factuality and Deception in German?](https://mlsanity.com/paper/2409.20165/) (2024)

## Abstract (arXiv)

Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication. Varying motivations across research fields lead to differences in the domain choices to study and in the conceptualization of deception, making it hard to compare models and build robust deception detection systems for a given language. With this paper, we improve this situation by surveying available English deception datasets which include domains like social media reviews, court testimonials, opinion statements on specific topics, and deceptive dialogues from online strategy games. We consolidate these datasets into a single unified corpus. Based on this resource, we conduct a correlation analysis of linguistic cues of deception across datasets to understand the differences and perform cross-corpus modeling experiments which show that a cross-domain generalization is challenging to achieve. The unified deception corpus (UNIDECOR) can be obtained from https://www.ims.uni-stuttgart.de/data/unidecor.