Algorithmic visibility and informational quality in counselling-related videos: A cross-platform analysis of YouTube and TikTok

作者：Zefeng Sun, Tianrui He · 发表于：Computers in Human Behavior Reports · 年份：2026 · DOI：10.1016/j.chbr.2026.101160 · 研究领域：Digital Mental Health Interventions、Impact of Technology on Adolescents、Social Media in Health Education

Background and Aims In algorithmically mediated video platforms, informational visibility does not necessarily align with professional quality. While prior research has examined online mental health content, less is known about how platform environments systematically associated with the relationship between counselling-related informational quality and engagement-based visibility. This study investigates counselling-related videos on YouTube and TikTok to examine this misalignment. Method A cross-sectional comparative content analysis was conducted on 185 counselling-related videos (TikTok: n = 94; YouTube: n = 91). Informational quality was assessed using four standardized instruments (mDISCERN, JAMA, GQS, and VIQI), capturing reliability, transparency, overall quality, and production characteristics. Ordinal logistic regression models were employed to examine platform differences while controlling content characteristics. Results Within the sampled content, YouTube videos tended to score higher than TikTok videos across all quality dimensions, and this difference remained significant after controlling for content characteristics. Content grounded in professional knowledge and research consistently achieved higher quality scores than experiential or unsubstantiated content. Engagement metrics showed weak or negligible associations with informational quality, indicating a systematic misalignment between engagement-based visibility and professional standards. Conclusion and I...