@inproceedings{zhao-etal-2023-panacea,
title = "{PANACEA}: An Automated Misinformation Detection System on {COVID}-19",
author = "Zhao, Runcong and
Arana-catania, Miguel and
Zhu, Lixing and
Kochkina, Elena and
Gui, Lin and
Zubiaga, Arkaitz and
Procter, Rob and
Liakata, Maria and
He, Yulan",
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.9",
doi = "10.18653/v1/2023.eacl-demo.9",
pages = "67--74",
abstract = "In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.",
}
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<abstract>In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.</abstract>
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%0 Conference Proceedings
%T PANACEA: An Automated Misinformation Detection System on COVID-19
%A Zhao, Runcong
%A Arana-catania, Miguel
%A Zhu, Lixing
%A Kochkina, Elena
%A Gui, Lin
%A Zubiaga, Arkaitz
%A Procter, Rob
%A Liakata, Maria
%A He, Yulan
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhao-etal-2023-panacea
%X In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.
%R 10.18653/v1/2023.eacl-demo.9
%U https://aclanthology.org/2023.eacl-demo.9
%U https://doi.org/10.18653/v1/2023.eacl-demo.9
%P 67-74
Markdown (Informal)
[PANACEA: An Automated Misinformation Detection System on COVID-19](https://aclanthology.org/2023.eacl-demo.9) (Zhao et al., EACL 2023)
ACL
- Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, and Yulan He. 2023. PANACEA: An Automated Misinformation Detection System on COVID-19. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 67–74, Dubrovnik, Croatia. Association for Computational Linguistics.