Fake news, false or misleading information presented as news, has a great impact on many aspects of society, such as politics and healthcare. To handle this emerging problem, many fake news detection methods have been proposed, applying Natural Language Processing (NLP) techniques on the article text. Considering that even people cannot easily distinguish fake news by news content, these text-based solutions are insufficient. To further improve fake news detection, researchers suggested graph-based solutions, utilizing the social context information such as user engagement or publishers information. However, existing graph-based methods still suffer from the following four major drawbacks: 1) expensive computational cost due to a large number of user nodes in the graph, 2) the error in sub-tasks, such as textual encoding or stance detection, 3) loss of rich social context due to homogeneous representation of news graphs, and 4) the absence of temporal information utilization. In order to overcome the aforementioned issues, we propose a novel social context aware fake news detection method, Hetero-SCAN, based on a heterogeneous graph neural network. Hetero-SCAN learns the news representation from the heterogeneous graph of news in an end-to-end manner. We demonstrate that Hetero-SCAN yields significant improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency.
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