  The viral spread of fake news has caused great social harm, making fake news
detection an urgent task. Current fake news detection methods rely heavily on
text information by learning the extracted news content or writing style of
internal knowledge. However, deliberate rumors can mask writing style,
bypassing language models and invalidating simple text-based models. In fact,
news articles and other related components (such as news creators and news
topics) can be modeled as a heterogeneous information network (HIN for short).
In this paper, we propose a novel fake news detection framework, namely
Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical
attention mechanism to perform node representation learning in HIN, and then
detects fake news by classifying news article nodes. Experiments on two
real-world fake news datasets show that HGAT can outperform text-based models
and other network-based models. In addition, the experiment proved the
expandability and generalizability of our for graph representation learning and
other node classification related applications in heterogeneous graphs.
