DETECTING COVID-19 MISINFORMATION ON SOCIAL MEDIA

The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective in evaluating systems designed to detect misinfor-mation on this topic. Misinformation detection can be subdivided into two sub-tasks - retrieval of misconceptions relevant to posts being checked for veracity, and stance detection to identify whether the posts agree , disagree , or express no stance towards the retrieved mis-conceptions. To facilitate research on this task, we release COVID-Lies 1 , a dataset of 5K expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misin-formation. We evaluate existing NLP systems on this dataset, providing first benchmarks and identifying key challenges for future models to improve upon.