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

TL;DR

This paper introduces a language-independent graph-based method that leverages direct and indirect hashtag relationships to detect fake news early on social media, demonstrating effectiveness on real datasets.

Contribution

It proposes a novel graph capturing all hashtag relationships and a semi-supervised algorithm for fake news detection that is language-independent and extends to any homogeneous graph.

Findings

Effective in early fake news detection

Works across multiple languages

Outperforms baseline methods

Abstract

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.