Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information

Fake news, false or misleading information presented as news, has a significant impact on many aspects of society, such as in politics or healthcare domains. Due to the deceiving nature of fake news, applying Natural Language Processing (NLP) techniques to the news content alone is insufficient. Therefore, more information is required to improve fake news detection, such as the multi-level social context (news publishers and engaged users in social media) information and the temporal information of user engagement. The proper usage of this information, however, introduces three chronic difficulties: 1) multi-level social context information is hard to be used without information loss, 2) temporal information of user engagement is hard to be used along with multi-level social context information, and 3) news representation with multi-level social context and temporal information is hard to be learned in an end-to-end manner. To overcome all three difficulties, we propose a novel fake news detection framework, Hetero-SCAN. We use Meta-Path, a composite relation connecting two node types, to extract meaningful multi-level social context information without loss. We then propose Meta-Path instance encoding and aggregation methods to capture the temporal information of user engagement and learn news representation end-to-end. According to our experiment, Hetero-SCAN yields significant performance improvement over state-of-the-art fake news detection methods.