  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 a better understanding of how these articles are structured.
Nevertheless, this has rarely been investigated in the fake news detection
domain and faces tremendous challenges. First, existing methods for capturing
discourse-level structure rely on annotated corpora which are not available for
fake news datasets. Second, how to extract out useful information from such
discovered structures is another challenge. To address these challenges, we
propose Hierarchical Discourse-level Structure for Fake news detection. HDSF
learns and constructs a discourse-level structure for fake/real news articles
in an automated and data-driven manner. Moreover, we identify insightful
structure-related properties, which can explain the discovered structures and
boost our understating of fake news. Conducted 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 structures.
