  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 are publicly
available. Experimental results demonstrate that our model significantly
outperforms state-of-the-art baselines and generates high-quality explanations
from diverse evaluation perspectives.
