Characterization of Patient Reported COVID-19 Experiences Based on Reddit Data: A Qualitative Analysis

Background: Patients use social media forums to discuss their medical history and healthcare experiences, providing early insight into real-world patient experiences. We analyzed COVID-19 patient experiences from Reddit social media posts. Methods: We extracted Reddit Application Programming Interface data for the subreddit r/covid19positive from March to August 2020 and selected users tagged as “Tested Positive” or “Tested Positive- Me” flair and who posted at least thirty times in any calendar month, excluding users who explicitly stated location outside of the U.S. For tested-positive patients, we created and reviewed individual case profiles summarizing their COVID-19 symptoms, testing, and medications or treatments. Data were imported to Nvivo qualitative analysis software and qualitative coding was conducted. Finding: There were 31,759 posts and comments from 720 users in March-May 2020 (Q1) and 40,446 posts and comments from 1,649 users from June to August 2020 (Q2). Final count of “Tested Positive” was 1,296 users (280 in Q1 and 1,016 in Q2). Of those who reported age, over 90% were under the age of 40 years, with the majority in their 20s. There were more females (54%) than males (46%) among those who reported their gender. Across both quarters, most reported symptoms were throat pain, headaches, fevers, or chills. Loss of taste or smell, aches or pains and fever, chills were the frequently reported CDC-listed symptoms in Q2. Patients also reported in-depth descriptions of their symptoms, motivations for testing, and long-term impacts such as post-viral fatigue. Interpretation: Social media data can provide early preliminary insights into patient disease experiences and may supplement data captured in electronic healthcare databases. However, there are unique challenges in processing the data to capture pertinent information. Machine learning methods, such as natural language processing may improve the ability to capture relevant data from these large volume datasets.Funding Information: This project was supported in part by an appointment to the Research Participation Program at the U.S. Food and Drug Administration administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. Declaration of Interests: All others have nothing to declare.