Social Media and Men’s Health: A Content Analysis of <i>Twitter</i> Conversations During the 2013 Movember Campaigns in the United States, Canada, and the United Kingdom
Bibliographic record
Abstract
The Movember Foundation raises awareness and funds for men's health issues such as prostate and testicular cancers in conjunction with a moustache contest. The 2013 Movember campaigns in the United States, Canada, and the United Kingdom shared the same goal of creating conversations about men's health that lead to increased awareness and understanding of the health risks men face. Our objective was to explore Twitter conversations to identify whether the 2013 Movember campaigns sparked global conversations about prostate cancer, testicular cancer, and other men's health issues. We conducted a content analysis of 12,666 tweets posted during the 2013 Movember campaigns in the United States, Canada, and the United Kingdom (4,222 tweets from each country) to investigate whether tweets were health-related or non-health-related and to determine what topics dominated conversations. Few tweets ( n = 84, 0.7% of 12,666 tweets) provided content-rich or actionable health information that would lead to awareness and understanding of men's health risks. While moustache growing and grooming was the most popular topic in U.S. tweets, conversations about community engagement were most common in Canadian and U.K. tweets. Significantly more tweets co-opted the Movember campaign to market products or contests in the United States than Canada and the United Kingdom ( p < .05). Findings from this content analysis of Twitter suggest that the 2013 Movember campaigns in the United States, Canada, and the United Kingdom sparked few conversations about prostate and testicular cancers that could potentially lead to greater awareness and understanding of important men's health issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".