Abstract
Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing interest in automatic fact-checking, it is still unclear if automated approaches can realistically refute harmful real-world misinformation. Here, we contrast and compare NLP fact-checking with how professional fact-checkers combat misinformation in the absence of counter-evidence. In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims. We then define two requirements that the evidence in datasets must fulfill for realistic fact-checking: It must be (1) sufficient to refute the claim and (2) not leaked from existing fact-checking articles. We survey existing fact-checking datasets and find that all of them fail to satisfy both criteria. Finally, we perform experiments to demonstrate that models trained on a large-scale fact-checking dataset rely on leaked evidence, which makes them unsuitable in real-world scenarios. Taken together, we show that current NLP fact-checking cannot realistically combat real-world misinformation because it depends on unrealistic assumptions about counter-evidence in the data.- Anthology ID:
- 2022.emnlp-main.397
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5916–5936
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.397
- DOI:
- 10.18653/v1/2022.emnlp-main.397
- Cite (ACL):
- Max Glockner, Yufang Hou, and Iryna Gurevych. 2022. Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5916–5936, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation (Glockner et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.397.pdf