ArticleDigital health
A decade of English-language YouTube discourse on fibromyalgia (2015-2025): Seasonal patterns, sentiment dynamics, and methodological considerations.
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5 authors.
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Abstract
Background: Fibromyalgia is a chronic pain syndrome affecting 2-4% of the global population. Social media platforms host patient narratives and public discussions that can complement traditional evidence. Objective: To characterize English-language YouTube discourse on fibromyalgia over 2015-2025, describing longitudinal and seasonal engagement patterns and sentiment dynamics, while outlining platform- and translation-related methodological considerations. Methods: A retrospective cross-sectional analysis was conducted on YouTube videos referencing fibromyalgia (2015-2025) identified via English keywords. When non-English videos appeared, relevant comments were processed using a documented translation pipeline prior to analysis. Counts of videos, comments, and words were summarized; seasonal groups were compared. Sentiment classification (negative/neutral/positive) was performed using an automated model; searches were repeated in anonymized sessions to gauge recommendation-system effects, and a small in-study manual validation was conducted. Results: A total of 941 videos and 30,896 comments (1,505,579 words) were analyzed. Engagement peaked in spring (12,125 comments) and summer (8899). Neutral sentiment predominated across seasons (62.1-66.3%), with relatively higher positive proportions in summer (25.3%). Seasonal differences in engagement were statistically significant in nonparametric comparisons. Manual validation indicated good overall performance of the sentiment model, with reduced accuracy on translated comments. Conclusions: English-language YouTube discussions on fibromyalgia increased over the decade and exhibited seasonal patterns. Findings should be interpreted as platform-specific and noncausal, given recommendation-system influences, language constraints, and automated sentiment limitations. Nevertheless, social media analytics can inform patient-centered communication and hypotheses for future research.
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