Cochrane Skin Group’s Global Social Media Reach: Content Analysis of Facebook, Instagram, and Twitter Posts
Bibliographic record
Abstract
BACKGROUND: Researchers in all medical specialties increasingly use social media to educate the public, share new publications with peers, and diversify their audiences. OBJECTIVE: Given Cochrane Skin Group's expanded use of social media in the past years, we aimed to characterize Cochrane Skin Group's international social media audience and identify themes that result in increased content engagement. METHODS: Cochrane Skin Group's Facebook, Instagram, and Twitter analytics data were extracted for follower demographics and the most viewed posts within a 3-year span (June 2019 to June 2022). RESULTS: Overall, Cochrane Skin Group had the highest number of followers on Facebook (n=1037). The number of Instagram and Twitter followers reached 214 and 352, respectively. The greatest numbers of Facebook followers were from Brazil, Egypt, and India, with 271, 299, and 463 followers, respectively. Facebook's most viewed post about Cochrane Skin Group's annual meeting received 1041 views. The top post on Instagram, which introduced Cochrane Skin Group's social media editors, received 2522 views. CONCLUSIONS: Each of the social media platforms used by Cochrane Skin Group reached varying audiences all over the world. Across social media platforms, posts regarding Cochrane Skin Group meetings, members, and professional opportunities received the most views. Overall, Cochrane Skin Group's multiplatform social media approach will continue to grow an international audience, connecting people interested in skin disease.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".