Learning Hierarchical Discourse-level Structure for Fake News Detection
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On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges: existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets as well as how and what insightful information can be extracted from such discovered structures. To address these challenges, we propose Discourse-level Hierarchical Structure for Fake news detection. DHSF constructs discourse-level structures of fake/real news articles in an automated manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Extensive experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structure.
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