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

通过构建多种异质社交上下文图并将问题重新定义为图分类任务，本文提出了一种高效的方法来检测虚假新闻，并在常见基准数据集上获得了鲁棒的结果，研究成果表明将不同类型的信息（以获得何种社交上下文水平最有效）与使用不同的图形神经网络架构相结合非常有效。