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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 fakenews. To this end, we propose a method to infer news credibility from hashtagsinvolved in news dissemination on social media, motivated by the tightconnection between hashtags and news credibility observed in our empiricalanalyses. We first introduce a new graph that captures all (direct and\textit{indirect}) relationships among hashtags. Then, a language-independentsemi-supervised algorithm is developed to predict fake news based on thisconstructed graph. This study first investigates the indirect relationshipamong hashtags; the proposed approach can be extended to any homogeneous graphto capture a comprehensive relationship among nodes. Language independenceopens the proposed method to multilingual fake news detection. Experimentsconducted on two real-world datasets demonstrate the effectiveness of ourapproach in identifying fake news, especially at an \textit{early} stage ofpropagation.

From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection