Observational studyJournal of medical Internet research2025
Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos.
Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well-Being.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026Pooled it
- When sleep advice goes viral: medical reliability of insomnia content on TikTok.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Article
- Quality assessment of ADHD-related short videos on Chinese social media: A cross-sectional study.Medicine · 2026Article
- Are antidepressants held to a different standard?BJPsych open · 2026Article
- Large Language Model-Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support.Journal of medical Internet research · 2026Article
- Article
- Influencing the Influencers: How Health Experts Are Partnering With Content Creators to Fight Misinformation Online.Journal of medical Internet research · 2026Article
- Effectiveness and Experiences of Online Mental Health Peer Support for Young People: Systematic Scoping Review.JMIR mental health · 2026Review
- Debate: Standing up for science - how to combat misinformation in child mental health? Five recommendations for disentangling fact from fiction.Child and adolescent mental health · 2026Article
- Evaluating a Culturally Tailored Digital Storytelling Intervention to Improve Trauma Awareness in Conflict-Affected Eastern Congo: Quasi-Experimental Pilot Study.JMIR mental health · 2026Article
- Perceived Disinformation, Anxiety, and Depressive Symptoms in the Czech Population: Age-Specific Associations.Depression and anxiety · 2026Article
- Knowledge, Attitude, and Practice Towards Urticaria in an Online Sample of the Chinese General Population.Acta dermato-venereologica · 2025Article
- Quality and reliability of kidney stone information on TikTok and Bilibili: A cross-sectional study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Disinformation on social media can seriously affect mental health by spreading false information, increasing anxiety, stress, and confusion in vulnerable individuals, as well as perpetuating stigma. This flood of misleading content can undermine trust in reliable sources and heighten feelings of isolation and helplessness among users. Objective: This study aimed to explore the phenomenon of disinformation about mental health on social media and provide recommendations to mental health professionals that would use social media platforms to create educational videos about mental health topics. Methods: A comprehensive analysis conducted on 1000 TikTok videos from more than 16 countries, available in English, French, and Spanish, covering 26 mental health topics. The data collection was conducted using a framework on disinformation and social media. A multilayered perceptron algorithm was used to identify factors predicting disinformation. Recommendations to health professionals about the creation of informative mental health videos were designed as per the data collected. Results: Disinformation was predominantly found in videos about neurodevelopment, mental health, personality disorders, suicide, psychotic disorders, and treatment. A machine learning model identified weak predictors of disinformation, such as an initial perceived intent to disinform and content aimed at the general public rather than a specific audience. Other factors, including content presented by licensed professionals such as a counseling resident, an ear-nose-throat surgeon, or a therapist, and country-specific variables from Ireland, Colombia, and the Philippines, as well as topics such as adjustment disorder, addiction, eating disorders, and impulse control disorders, showed a weak negative association with disinformation. In terms of engagement, only the number of favorites was significantly associated with a reduction in disinformation. Five recommendations were made to enhance the quality of educational videos about mental health on social media platforms. Conclusions: This study is the first to provide specific, data-driven recommendations to mental health providers globally, addressing the current state of disinformation on social media. Further research is needed to assess the implementation of these recommendations by health professionals, their impact on patient health, and the quality of mental health information on social networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.