GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Yi-Ju Lu, Cheng-Te Li


Abstract
This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.
Anthology ID:
2020.acl-main.48
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
505–514
Language:
URL:
https://aclanthology.org/2020.acl-main.48
DOI:
10.18653/v1/2020.acl-main.48
Bibkey:
Cite (ACL):
Yi-Ju Lu and Cheng-Te Li. 2020. GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 505–514, Online. Association for Computational Linguistics.
Cite (Informal):
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (Lu & Li, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.48.pdf
Video:
 http://slideslive.com/38928882
Code
 l852888/GCAN