Evaluating the quality, reliability, and diagnostic risk of ADHD content on TikTok and Bilibili: A cross-sectional...

Attention-Deficit/Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder requiring professional diagnosis. Recently, short-video platforms such as TikTok and Bilibili have seen a surge in ADHD-related content, driving a trend of self-diagnosis among the public, particularly young adults. The scientific quality and potential risks of this content have not been systematically evaluated. This study aimed to systematically evaluate the quality and reliability of ADHD content on TikTok and Bilibili, analyze its content characteristics, and specifically investigate the prevalence of content encouraging self-diagnosis and its association with user engagement.

The top 100 videos from each platform were retrieved using the keywords "ADHD" and "." After a screening process, a total of 164 videos were included for analysis. Two senior clinical psychologists independently assessed the videos using the modified DISCERN (mDISCERN) tool and the Global Quality Score (GQS). Videos were classified by uploader type (e.g., healthcare professionals, patients/influencers) and content theme (e.g., symptom education, self-tests). A novel Self-Diagnosis Risk Scale (SDRS) was also applied. Nonparametric statistical methods were used for data analysis.

A total of 164 videos were analyzed (88 from TikTok, 76 from Bilibili). Significant platform differences emerged, with Bilibili videos demonstrating superior quality scores (GQS: 3.05 ± 0.91 vs. 2.45 ± 0.88; mDISCERN: 2.62 ± 0.85 vs. 1.88 ± 0.72; both p < 0.001) but TikTok videos showing higher self-diagnosis risk (SDRS: 1.71 ± 0.51 vs. 1.30 ± 0.69; p < 0.001). Healthcare professionals produced the highest quality content (GQS: 3.65 ± 0.68; mDISCERN: 3.15 ± 0.81) with lowest diagnostic risk (SDRS: 0.75 ± 0.49), while patients/influencers created content with the lowest quality and highest risk scores. Critically, a "quality-engagement paradox" was identified: videos with higher self-diagnosis risk received significantly more user engagement (likes: r = 0.45, p < 0.001; shares: r = 0.42, p < 0.001), while quality metrics showed no significant correlation with user engagement measures.

This study reveals concerning patterns in ADHD-related content on major Chinese short-video platforms, where potentially harmful content encouraging self-diagnosis receives preferential algorithmic promotion over scientifically rigorous material. The inverse relationship between content quality and user engagement suggests current platform mechanisms may inadvertently amplify misleading health information while marginalizing evidence-based content. These findings underscore the urgent need for collaborative interventions involving platform operators, healthcare professionals, and public health educators to develop content guidelines, improve algorithmic curation of health information, and support healthcare professionals in creating engaging, evidence-based content. As social media platforms continue serving as primary health information sources, ensuring quality and safety of mental health content must become a priority for platform governance and public health policy.