  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.
