PANACEA: An Automated Misinformation Detection System on COVID-19
This addresses the problem of misinformation spread during the COVID-19 pandemic for public health and online users, offering an automated detection tool.
The paper tackles COVID-19 misinformation by developing PANACEA, a web-based system with fact-checking and rumour detection modules; the fact-checking module outperforms state-of-the-art approaches using novel natural language inference methods, and the rumour detection module uses graph networks to identify rumours from tweet comment networks.
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.