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The rapid evolution of the COVID-19 pandemic has underscored the need toquickly disseminate the latest clinical knowledge during a public-healthemergency. One surprisingly effective platform for healthcare professionals(HCPs) to share knowledge and experiences from the front lines has been socialmedia (for example, the "#medtwitter" community on Twitter). However,identifying clinically-relevant content in social media without manual labelingis a challenge because of the sheer volume of irrelevant data. We present anunsupervised, iterative approach to mine clinically relevant information fromsocial media data, which begins by heuristically filtering for HCP-authoredtexts and incorporates topic modeling and concept extraction with MetaMap. Thisapproach identifies granular topics and tweets with high clinical relevancefrom a set of about 52 million COVID-19-related tweets from January to mid-June2020. We also show that because the technique does not require manual labeling,it can be used to identify emerging topics on a week-to-week basis. Our methodcan aid in future public-health emergencies by facilitating knowledge transferamong healthcare workers in a rapidly-changing information environment, and byproviding an efficient and unsupervised way of highlighting potential areas forclinical research.

Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media