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Existing fake news detection methods aim to classify a piece of news as trueor 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 newshas not yet been fact-checked or debunked, certain amounts of relevant rawreports are usually disseminated on various media outlets, containing thewisdom of crowds to verify the news claim and explain its verdict. In thispaper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation(CofCED) neural network for explainable fake news detection based on such rawreports, alleviating the dependency on fact-checked ones. Specifically, wefirst utilize a hierarchical encoder for web text representation, and thendevelop two cascaded selectors to select the most explainable sentences forverdicts on top of the selected top-K reports in a coarse-to-fine manner.Besides, we construct two explainable fake news datasets, which are publiclyavailable. Experimental results demonstrate that our model significantlyoutperforms state-of-the-art baselines and generates high-quality explanationsfrom diverse evaluation perspectives.

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