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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 anarticle. Many other entities, such as news creators, news subjects, and so on,exist on social media and have relationships with news articles. Differententities and relationships can be modeled as a heterogeneous informationnetwork (HIN). In this paper, we attempt to solve the fake news detectionproblem with the support of a news-oriented HIN. We propose a novel fake newsdetection framework, namely Adversarial Active Learning-based HeterogeneousGraph Neural Network (AA-HGNN) which employs a novel hierarchical attentionmechanism to perform node representation learning in the HIN. AA-HGNN utilizesan active learning framework to enhance learning performance, especially whenfacing the paucity of labeled data. An adversarial selector will be trained toquery high-value candidates for the active learning framework. When theadversarial active learning is completed, AA-HGNN detects fake news byclassifying news article nodes. Experiments with two real-world fake newsdatasets show that our model can outperform text-based models and othergraph-based models when using less labeled data benefiting from the adversarialactive learning. As a model with generalizability, AA-HGNN also has the abilityto be widely used in other node classification-related applications onheterogeneous graphs.

Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection