A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, Yi Chang


Abstract
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives.
Anthology ID:
2022.coling-1.230
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2608–2621
Language:
URL:
https://aclanthology.org/2022.coling-1.230
DOI:
Bibkey:
Cite (ACL):
Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, and Yi Chang. 2022. A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2608–2621, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (Yang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.230.pdf
Code
 nicozwy/cofced
Data
FEVERLIAR