Content Analysis of Instagram Stories of Top Plastic Surgeons
Bibliographic record
Abstract
Abstract Instagram stories (Meta, Menlo Park, CA) are posted at higher volumes and incite increased engagement for accounts. This is because of Instagram's algorithm, which typically only shows static posts to 10% of an account's followers. Given the importance of social media in cosmetic surgery practices, a detailed analysis of trends is useful in guiding plastic surgeon marketing regimes. The aim of the authors of this study is to provide a detailed analysis of Instagram story content posted by top 100 most-followed plastic surgeons in the world. One hundred plastic surgery Instagram accounts in the world were identified by their total number of followers and using the keywords “plastic,” “cosmetic,” “aesthetic,” and “surgeon.” Accounts of each surgeon were monitored daily over 1 week, with story content categorically logged. Broad categories included personal, educational, surgical, and engagement, which were further divided into subcategories. Qualitative and quantitative measures were used to assess demographics and trends. The average number of followers by the top accounts was 437,917 ± 36,216. The majority of accounts were located in North America (64%). The average number of stories posted each week was 28.2 ± 3.7, with the highest number posted on Friday (5.01) and the lowest on Saturday (2.67). The most common content of stories posted was related to surgical procedures (57.6%), contributing 1577 total stories. In this descriptive study, the authors provide insight into the nature of Instagram story content posted by top-followed plastic surgeons in the world, providing guidance to new and existing plastic surgeons in their social media practices. Level of Evidence: 5 (Therapeutic):
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".