Using YouTube Comments Data to Explore Postpartum Depression in Social Media: An Infodemiology Study
Bibliographic record
Abstract
BACKGROUND: Postpartum depression (PPD) is a prevalent mental health issue profoundly impacting both parents and their families. This study examines YouTube comments to identify common public discourse and prevalent themes surrounding PPD. METHODS: We analyzed 4915 comments from 33 YouTube videos to provide a comprehensive picture of PPD-related discourse on social media. We analyzed data using engagement metrics and Braun and Clarke's thematic analysis. RESULTS: The engagement metrics indicated that public discourse is primarily focused on the stigma associated with PPD in men and celebrities, with related videos receiving significant attention and high engagement metrics score. Thematic analysis revealed two themes: (1) perspectives of stigmatized, stigmatizer and people in between; and (2) adaptation despite adversity. CONCLUSION: This study provides key insights into public discourse on PPD. It highlights the importance of family and community support and advocates for a healthcare system capable of addressing the needs of stigmatized populations. A significant finding of this study is the call for action to raise awareness and debunk myths about PPD. Misconceptions worsen stigma and deter help-seeking by affected individuals. Awareness initiatives are crucial to enhance public understanding of PPD symptoms, its impact on individuals and families, and the importance of parental mental health.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".