Learning Hierarchical Discourse-level Structure for Fake News Detection

Hamid Karimi, Jiliang Tang


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.
Anthology ID:
N19-1347
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3432–3442
Language:
URL:
https://aclanthology.org/N19-1347
DOI:
10.18653/v1/N19-1347
Bibkey:
Cite (ACL):
Hamid Karimi and Jiliang Tang. 2019. Learning Hierarchical Discourse-level Structure for Fake News Detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3432–3442, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Learning Hierarchical Discourse-level Structure for Fake News Detection (Karimi & Tang, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1347.pdf
Code
 hamidkarimi/HDSF +  additional community code