PANACEA: An Automated Misinformation Detection System on COVID-19

Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages67–74
Publication statusPublished - May 2023

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