Learning Hierarchical Discourse-level Structure for Fake News Detection

TL;DR

This paper introduces a novel data-driven method to learn and analyze hierarchical discourse structures in fake and real news articles, improving detection accuracy and understanding of their structural differences.

Contribution

It proposes Hierarchical Discourse-level Structure for Fake news detection (HDSF), an automated approach to construct and analyze discourse structures without annotated data.

Findings

Discovered significant structural differences between fake and real news.

HDSF improves fake news detection performance.

Structural properties explain differences in news authenticity.

Abstract

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.