@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Entity-Aware Dual Co-Attention Network for Fake News Detection
%A Yang, Sin-han
%A Chen, Chung-chi
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yang-etal-2023-entity
%X 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.
%R 10.18653/v1/2023.findings-eacl.7
%U https://aclanthology.org/2023.findings-eacl.7
%U https://doi.org/10.18653/v1/2023.findings-eacl.7
%P 106-113
Markdown (Informal)
[Entity-Aware Dual Co-Attention Network for Fake News Detection](https://aclanthology.org/2023.findings-eacl.7) (Yang et al., Findings 2023)
ACL