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It will appear shortly on 10.2196/85323
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Depression Portrayals on TikTok: Engagement Patterns as a Function of Diagnostic Accuracy, Creator Type, and Stylistic Features
ABSTRACT
Background:
Youth increasingly turn to TikTok for mental health information, making the platform an important place where young people encounter portrayals of mental illness. While such visibility can raise awareness and reduce stigma, concerns have been raised about the accuracy of the content, which is often produced by non-professionals and presented with emotionally appealing stylistic features. Prior research has examined mental health content on TikTok broadly, but little is known about how specific disorders such as depression are portrayed.
Objective:
Given depression’s rising prevalence among youth and its prominent featuring on TikTok, this study analyzes the diagnostic accuracy and stylistic features of depression-related videos, how these differ between creator types (medical professionals versus non-professionals), and how these factors relate to user engagement.
Methods:
Using ICD-11 criteria to assess diagnostic accuracy, a quantitative content analysis of 210 TikToks was conducted.
Results:
Findings reveal that, overall, diagnostic accuracy was low and did not differ significantly between medical professionals and non-professionals. While low accuracy alone did not predict higher engagement, the presence of stylistic features such as emotional appeals, personal experiences, and background music were all associated with significantly higher engagement. Conversely, videos by medical professionals and those featuring certain presentation styles (speaking directly to the camera or text-based formats) predicted lower engagement.
Conclusions:
These results raise concerns about concept creep—the gradual expansion of the psychological concept for depression—and the potential for premature self-diagnosis among young users. They also highlight the need for medical professionals to adapt their communication styles on TikTok to increase both accuracy and engagement. Clinical Trial: N/A
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.