Post-traumatic stress disorder on TikTok: A cross-sectional analysis of popular #PTSD content quality (Preprint)
Year: 2025
Type: preprint
Abstract: BACKGROUND Attentional problems are a non-specific symptom that occurs in both Attention-deficit/hyperactivity disorder (ADHD) and Post-traumatic stress disorder (PTSD). A recent study reported that over 50% of the most-popular content on TikTok under the hashtag #ADHD was classified as misleading (Yeung et al., 2022). TikTok’s growth during the COVID-19 pandemic, its role as a source of social support during periods of social isolation, and the tendency for individuals to research their symptoms online before seeking treatment raise the question of how accurate PTSD-related content is in conveying information about symptoms, diagnosis, and treatment (Şot, 2023). OBJECTIVE The aim of the current study was to characterize the accuracy, understandability, and actionability of PTSD-related TikTok content. METHODS We conducted a search on TikTok using the hashtag #PTSD and retrieved the 100 most-liked videos. Metadata (e.g., views, shares) were collected using the publicly available Python package TiktokPy. Videos were independently coded by two reviewers for the overall accuracy of the video (classified as useful, personal experience, or misleading), whether the creator self-identified as a healthcare professional (HCP), symptoms mentioned, and overall video understandability and actionability using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-AV). RESULTS Approximately one-third of the most-popular TikTok content related to PTSD contained misleading information. The accuracy of PTSD-related information was not associated with its understandability, actionability, or user engagement. Videos created by self-identified HCPs were more likely to contain useful information than those created by non-HCPs; however, HCPs accounted for 32% of the content classified as misleading. CONCLUSIONS TikTok’s algorithmically determined, adaptive content delivery method has the potential to inundate users with non-specific mental health information, even without explicit searches for such content. This system may incentivize the distribution of highly salient or unique content, which may not necessarily align with accuracy. These factors could influence patients’ interpretations of their symptoms and ultimately their clinical presentations. Clinicians should be aware of the digital landscape surrounding the conditions they treat to provide informed care.
Language: English
Subfield: Health
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4406482272