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Fake news gains has gained significant momentum, strongly motivating the needfor fake news research. Many fake news detection approaches have thus beenproposed, where most of them heavily rely on news content. However,network-based clues revealed when analyzing news propagation on social networksis an information that has hardly been comprehensively explored or used forfake news detection. We bridge this gap by proposing a network-basedpattern-driven fake news detection approach. We aim to study the patterns offake news in social networks, which refer to the news being spread, spreadersof the news and relationships among the spreaders. Empirical evidence andinterpretations on the existence of such patterns are provided based on socialpsychological theories. These patterns are then represented at various networklevels (i.e., node-level, ego-level, triad-level, community-level and theoverall network) for being further utilized to detect fake news. The proposedapproach enhances the explainability in fake news feature engineering.Experiments conducted on real-world data demonstrate that the proposed approachcan outperform the state of the arts.

Network-based Fake News Detection: A Pattern-driven Approach