Entity-Aware Dual Co-Attention Network for Fake News Detection

Sin-han Yang, Chung-chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.
Anthology ID:
2023.findings-eacl.7
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–113
Language:
URL:
https://aclanthology.org/2023.findings-eacl.7
DOI:
10.18653/v1/2023.findings-eacl.7
Bibkey:
Cite (ACL):
Sin-han Yang, Chung-chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2023. Entity-Aware Dual Co-Attention Network for Fake News Detection. In Findings of the Association for Computational Linguistics: EACL 2023, pages 106–113, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Entity-Aware Dual Co-Attention Network for Fake News Detection (Yang et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-eacl.7.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.7.mp4