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Accepted for/Published in: JMIR AI

Date Submitted: Nov 14, 2023
Date Accepted: Apr 6, 2024

The final, peer-reviewed published version of this preprint can be found here:

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

Jordan A, Park A

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

JMIR AI 2024;3:e54501

DOI: 10.2196/54501

PMID: 38875666

PMCID: 11184269

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Understanding COVID-19 Long Haulers: Computational Analysis of YouTube Content

  • Alexis Jordan; 
  • Albert Park

ABSTRACT

Background:

The coronavirus (COVID-19) pandemic had a devastating global impact. In the United States, there were more than 98 million COVID-19 cases and over 1 million resulting deaths. One consequence of COVID-19 infection has been Coronavirus Post-Acute Sequelae (PASC). People with this syndrome, colloquially called Long Haulers, experience symptoms that impact their quality of life. The root cause of PASC 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 three different types of content creators: Medical sources, News sources, and Long Haulers. To computationally understand the video content and viewers’ reaction, 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’ reaction, we used VADER 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 Transcripts themes were Explanations in Layman’s Terms and Biological Explanations. Examples of News Transcripts themes were Negative Experiences and Handling the Long Haul. Two Long Hauler Transcripts 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/Disinformation and Issues with the Healthcare System. Similarly, negative Long Hauler comments were organized into several themes, including Disillusionment of Healthcare System and Requiring More Visibility. In contrast, positive Medical source comments captured themes such as Appreciation of Helpful Content, and Exchange of Helpful Information. Following this theme, one positive theme found in Long Hauler Comments was Community Building.

Conclusions:

Results of this study could help public health agencies, policymakers, organizations and health researchers to understand symptomatology and experiences related to Long Covid. It also helps these agencies to develop their communication strategy concerning Long Covid.


 Citation

Please cite as:

Jordan A, Park A

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

JMIR AI 2024;3:e54501

DOI: 10.2196/54501

PMID: 38875666

PMCID: 11184269

Per the author's request the PDF is not available.