The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media, motivated by the tight connection between hashtags and news credibility observed in our empirical analyses. We first introduce a new graph that captures all (direct and \textit{indirect}) relationships among hashtags. Then, a language-independent semi-supervised algorithm is developed to predict fake news based on this constructed graph. This study first investigates the indirect relationship among hashtags; the proposed approach can be extended to any homogeneous graph to capture a comprehensive relationship among nodes. Language independence opens the proposed method to multilingual fake news detection. Experiments conducted on two real-world datasets demonstrate the effectiveness of our approach in identifying fake news, especially at an \textit{early} stage of propagation.
翻译：COVID-19大流行已引起全世界的注意,并允许假消息,如“COVID-19是流感,”在社交媒体上迅速和广泛传播。 对付这种冠状病毒的流行需要有效的方法来检测假消息。 为此,我们提出一种方法来从社交媒体新闻传播中涉及的标签上推断新闻的可信度,其动机是标签与我们的经验分析中观察到的新闻可信度之间的紧密联系。 我们首先引入一个新图,它能捕捉到所有标签(直接和Textit{indirect})之间的关系。 然后,开发出一种依赖语言的半监督算法,以预测基于这个构建的图表的假消息。 这项研究首先调查各标签之间的间接关系; 提议的方法可以扩大到任何单一的图表,以捕捉各节点之间的全面关系。 语言独立开启了拟议的多语种假新闻探测方法。 在两个真实世界数据集上进行的实验显示了我们识别假消息的方法的有效性, 特别是在一个Textit{early) 传播阶段。
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