# How Entangled is Factuality and Deception in German?

arXiv: https://arxiv.org/abs/2409.20165 · HTML: https://mlsanity.com/paper/2409.20165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023)
- [Synthetic Disinformation Attacks on Automated Fact Verification Systems](https://mlsanity.com/paper/2202.09381/) (2022)
- [The Mafiascum Dataset: A Large Text Corpus for Deception Detection](https://mlsanity.com/paper/1811.07851/) (2018)
- [Automatic Fake News Detection: Are Models Learning to Reason?](https://mlsanity.com/paper/2105.07698/) (2021)
- [To Tell The Truth: Language of Deception and Language Models](https://mlsanity.com/paper/2311.07092/) (2023)
- [Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception](https://mlsanity.com/paper/2403.10185/) (2024)
- [Online Deception Detection Refueled by Real World Data Collection](https://mlsanity.com/paper/1707.09406/) (2017)
- [Automatic Fake News Detection: Are current models "fact-checking" or "gut-checking"?](https://mlsanity.com/paper/2204.07229/) (2022)
- [UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection](https://mlsanity.com/paper/2306.02827/) (2023)

## Abstract (arXiv)

The statement "The earth is flat" is factually inaccurate, but if someone truly believes and argues in its favor, it is not deceptive. Research on deception detection and fact checking often conflates factual accuracy with the truthfulness of statements. This assumption makes it difficult to (a) study subtle distinctions and interactions between the two and (b) gauge their effects on downstream tasks. The belief-based deception framework disentangles these properties by defining texts as deceptive when there is a mismatch between what people say and what they truly believe. In this study, we assess if presumed patterns of deception generalize to German language texts. We test the effectiveness of computational models in detecting deception using an established corpus of belief-based argumentation. Finally, we gauge the impact of deception on the downstream task of fact checking and explore if this property confounds verification models. Surprisingly, our analysis finds no correlation with established cues of deception. Previous work claimed that computational models can outperform humans in deception detection accuracy, however, our experiments show that both traditional and state-of-the-art models struggle with the task, performing no better than random guessing. For fact checking, we find that Natural Language Inference-based verification performs worse on non-factual and deceptive content, while prompting Large Language Models for the same task is less sensitive to these properties.