  Disinformation and fake news have posed detrimental effects on individuals
and society in recent years, attracting broad attention to fake news detection.
The majority of existing fake news detection algorithms focus on mining news
content and/or the surrounding exogenous context for discovering deceptive
signals; while the endogenous preference of a user when he/she decides to
spread a piece of fake news or not is ignored. The confirmation bias theory has
indicated that a user is more likely to spread a piece of fake news when it
confirms his/her existing beliefs/preferences. Users' historical, social
engagements such as posts provide rich information about users' preferences
toward news and have great potential to advance fake news detection. However,
the work on exploring user preference for fake news detection is somewhat
limited. Therefore, in this paper, we study the novel problem of exploiting
user preference for fake news detection. We propose a new framework, UPFD,
which simultaneously captures various signals from user preferences by joint
content and graph modeling. Experimental results on real-world datasets
demonstrate the effectiveness of the proposed framework. We release our code
and data as a benchmark for GNN-based fake news detection:
https://github.com/safe-graph/GNN-FakeNews.
