ABSTRACT
As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.