Learning from COVID-19: An AI-enabled evidence-driven framework for claim veracity assessment during pandemics

The term 'infodemic' coined by the WHO refers to misinformation during pandemics that can create panic, fragment social response, affect rates of transmission; encourage trade in untested treatments that put people's lives in danger. The WHO and government agencies have to divert significant resources to combat infodemics. Their scale makes it essential to employ computational techniques for claim veracity assessment. However, existing approaches largely rely on supervised learning. Present accuracy levels fall short of that required for practical adoption as training data is small and performance tends to degrade significantly on claims/topics unseen during training: current practices are unsuitable for addressing the scale and complexity of the COVID-19 infodemic. This project will research novel supervised/unsupervised methods for veracity assessment of claims unverified at the time of posting, by integrating information from multiple sources and building a knowledge network that enables cross verification. Key originating sources/agents will be identified through patterns of misinformation propagation and results will be presented via a novel visualisation interface for easy interpretation by users. This high-level aim gives rise to the following objectives: RO1. Collect COVID-19 related data from social media platforms and authoritative resources. RO2. Develop automated methods to extract key information on COVID-19 from scientific publications and other relevant sources. RO3. Develop novel unsupervised/supervised approaches for veracity assessment by incorporating evidence from external sources. RO4. Analyse dynamic spreading-patterns of rumour in social media; identify the key sources/agents and develop effective containment strategies. RO5. Validate the methods via a set of new visualisation interfaces.