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Fake news detection has become a research area that goes way beyond a purelyacademic interest as it has direct implications on our society as a whole.Recent advances have primarily focused on textbased approaches. However, it hasbecome clear that to be effective one needs to incorporate additional,contextual information such as spreading behaviour of news articles and userinteraction patterns on social media. We propose to construct heterogeneoussocial context graphs around news articles and reformulate the problem as agraph classification task. Exploring the incorporation of different types ofinformation (to get an idea as to what level of social context is mosteffective) and using different graph neural network architectures indicatesthat this approach is highly effective with robust results on a commonbenchmark dataset.

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs