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