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*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceeding

4 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Computational Linguistics
EditorsNicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
PublisherInternational Committee on Computational Linguistics
Number of pages14
Publication statusPublished - 17 Oct 2022
EventThe 29th International Conference on Computational Linguistics - Gyeongju, Korea, Republic of
Duration: 12 Oct 202217 Oct 2022

Publication series

NameProceedings of International Conference on Computational Linguistics
ISSN (Print)2951-2093


ConferenceThe 29th International Conference on Computational Linguistics
Abbreviated titleCOLING 2022
Country/TerritoryKorea, Republic of
Internet address


Dive into the research topics of 'A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection'. Together they form a unique fingerprint.

Cite this