  The explosive growth of fake news along with destructive effects on politics,
economy, and public safety has increased the demand for fake news detection.
Fake news on social media does not exist independently in the form of an
article. Many other entities, such as news creators, news subjects, and so on,
exist on social media and have relationships with news articles. Different
entities and relationships can be modeled as a heterogeneous information
network (HIN). In this paper, we attempt to solve the fake news detection
problem with the support of a news-oriented HIN. We propose a novel fake news
detection framework, namely Adversarial Active Learning-based Heterogeneous
Graph Neural Network (AA-HGNN) which employs a novel hierarchical attention
mechanism to perform node representation learning in the HIN. AA-HGNN utilizes
an active learning framework to enhance learning performance, especially when
facing the paucity of labeled data. An adversarial selector will be trained to
query high-value candidates for the active learning framework. When the
adversarial active learning is completed, AA-HGNN detects fake news by
classifying news article nodes. Experiments with two real-world fake news
datasets show that our model can outperform text-based models and other
graph-based models when using less labeled data benefiting from the adversarial
active learning. As a model with generalizability, AA-HGNN also has the ability
to be widely used in other node classification-related applications on
heterogeneous graphs.
