Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content.

Background: The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance.

Objective: In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience.

Methods: We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis.

Results: We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building.

Conclusions: The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.