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.

翻译：本文演示介绍了一个基于网络的COVID-19相关主张虚假信息检测系统PANACEA，该系统包含事实核查与谣言检测两个模块。其中，事实核查模块采用基于自注意力网络的新型自然语言推理方法，性能优于现有最优技术。该模块能够自动进行真实性评估，并依据对待核查主张的立场提供排序后的支撑证据。此外，PANACEA采用了双向图卷积网络模型，该模型无需依赖知识库，而是基于相关推文的评论网络实现谣言检测。此谣言检测模块可在知识库尚未就绪的早期阶段向用户发出预警，从而提供辅助支持。