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.
翻译:现有假新闻探测方法旨在将新闻归类为真实或虚假新闻,并提供真实的解释,取得显著的成绩。然而,它们往往在手工的经过事实检查的报告上定制自动化解决方案,因为新闻报道有限,而且拖延被揭发。当一件新闻尚未进行事实检查或揭发时,某些数量的有关原始报告通常会在各种媒体上传播,包含人群核实新闻主张和解释其判断的智慧。在本文中,我们提议建立一个新颖的Coarse-fine Cascad-evid Revication(CofCED)神经网络,以便根据这些原始报告来解释可解释的假新闻探测,减轻对经过事实检查的报告的依赖。具体地说,我们首先使用一个等级编码器来进行网络文本表述,然后开发两个级联式选择器,以粗略的方式选择最能解释的句子,在选定的最高K报告上作出判断。此外,我们还建两个可解释的假新闻数据集,可以公开提供。实验结果显示,我们的模型在高质量的基线和高质量下,大大超出模型。