Social media representation of female genital cutting: A YouTube analysis
Bibliographic record
Abstract
INTRODUCTION: Female genital cutting is a practice that has incited controversy and conflicting discourses across the international community. There is a need to analyze social media data on the portrayal of the practice in order to gather insights and inform strategic planning and interventions design. This study aims to explore and describe the portrayal of female genital cutting in the comments section of YouTube comment posts. METHODS: This mixed-method study employs a content analysis approach with a sequential exploratory design. A total of 150 YouTube comment posts were analyzed through qualitative content analysis and quantitative descriptive content analysis on NVivo 11 and Microsoft Excel, respectively. RESULTS: Salient subthemes from the qualitative component included likening female genital cutting with male genital cutting, differentiating female genital cutting from male genital cutting, branding female genital cutting as a harmful and unethical practice, branding female genital cutting as a normal tradition, contribution of religion and culture to female genital cutting, gender inequality issues, and the need for education or cultural relativism to change or cope with the practice. The quantitative component identified neutral, positive, mixed, and neutral tones; and formal, colloquial, and mixed language types; as well as targets of stigma with patterns in the themes. CONCLUSION: The portrayal of female genital cutting in the YouTube comment posts revealed the range of perceptions, beliefs, and opinions of users with various stances on the practice. Study findings are useful for strategic planning and the development of interventions with informative goals. Study findings can also help to gage and evaluate the effectiveness of existing programs that aim to reduce misinformation about female genital cutting or aim to reduce stigmas surrounding the practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".