Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information For Mental Health Professionals
Bibliographic record
Abstract
This study aims to investigate the spread and impact of disinformation on mental health on social media, specifically TikTok, and to develop strategies for mental health professionals to access reliable information. Disinformation about health on social media, including untested remedies and conspiracy theories, undermines public trust and health, making the fight against false information critical, especially during health emergencies. The project will analyze 1000 publicly available TikTok videos related to mental health, using specific inclusion criteria, without requiring ethics board approval since the videos are in the public domain. The study will collect data on video-related elements, fake news elements, and clinical psychiatric elements, ensuring a comprehensive analysis. Descriptive statistical analysis and qualitative content analysis will be conducted to identify disinformation trends and themes from viewers' perspectives. The study anticipates no risks, as all data are publicly accessible, and aims to enhance the ability of viewers to critically assess psycho-educational videos on mental health. The results will be shared in academic settings and aim to provide recommendations for creating informed and supportive online communities around psychiatry. The research team, led by Dr. Alexandre Hudon, comprises psychiatry professionals from Université de Montréal, ensuring the project's feasibility and integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".