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.

本文介绍一种名为PANACEA的基于自然语言推理和双向图卷积神经网络的在线虚假信息检测系统，包括事实核查和谣言检测两个模块。其中，事实检查模块通过自我关注网络支持的新颖自然语言推理方法，优于现有技术，可以自动评估真实性并排名支持证据与对应申述。而谣言检测模块则利用评论网络检测相关推文是否存在谣言，辅助用户在还没有知识库的早期阶段预警风险。

PANACEA: COVID-19自动化虚假信息检测系统