Using crowdsourced medicine to manage uncertainty on Reddit: The case of COVID-19 long-haulers

Abstract

Objective: Causes of and treatments for long-COVID syndrome remain unknown. Drawing on uncertainty management theory (UMT), this study elucidates the communicative nature of crowdsourced medicine as a means by which COVID "long-haulers" respond to their poorly understood illness.

Methods: 31,892 posts on the long-haulers subreddit (r/covidlonghaulers) were analyzed, starting with its creation date, July 24th, 2020, until January 7, 2021. The Meaning Extraction Method was used to identify clusters of words that mathematically group together across the text observations.

Results: Analyses yielded 16 distinct factors of words, which we thematized based on their composition, the data, and UMT. The 16 themes encompassed symptoms (e.g., pain, respiratory, sensory), diagnostic concerns (testing, diagnosis), broad health concerns (immunity, physical activity, diet), chronicity, support, identity, and anxiety.

Conclusion: Findings provide a succinct, yet robust set of themes reflecting the information-seeking (i.e., "This is happening to me") and support-seeking functions of long-haulers' talk (i.e., "Is this happening to you?"). Findings have implications for collective uncertainty management, online crowdsourcing, and patient advocacy.

Practice implications: We recommend that health care providers employ sensitivity when addressing the anxiety that long-haulers are experiencing while also validating that their physical symptoms are real. Online communities help long-haulers manage their uncertainty.

Keywords: COVID-19; Crowdsourcing; Social media; Uncertainty management.