@inproceedings{yang-etal-2023-entity,
    title = "Entity-Aware Dual Co-Attention Network for Fake News Detection",
    author = "Yang, Sin-han  and
      Chen, Chung-chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.findings-eacl.7/",
    doi = "10.18653/v1/2023.findings-eacl.7",
    pages = "106--113",
    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."
}