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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-eacl.7.pdf