Social Media as a Platform for Information and Support for Melanoma Patients: Analysis of Melanoma Facebook Groups and Pages
Bibliographic record
Abstract
Background: Social media is increasingly used as a source of health information and is useful for information exchange and patient support. Objective: The aim of this study is to describe the Facebook groups and pages that are available for melanoma patients. Methods: A systematic search of Facebook groups and pages was performed using the word “melanoma.” The first 50 pages found in the search, sorted by most relevant, were analyzed for several characteristics, namely page name, category, verification status, number of likes, number of followers, visitor posts per week, page posts per week, ability to donate, date of inception, and for-profit or nonprofit. The first 50 groups found in the search, sorted by most relevant, were analyzed for name, category, number of members, and privacy setting. Results: There were 669 pages and 568 groups related to melanoma found on Facebook. The first 50 pages had a combined total of 266,709 likes and 257,183 followers and, of these, 30% (15/50) were verified by Facebook. Within the analyzed Facebook pages, the average number of visitor posts per week was 0.48, the average number of posts by the page per week was 5.6, and the most common page categories were community and nonprofit. Of the 50 groups analyzed, 18 were public and 32 were private (closed). The total number of combined group members in all 50 groups was found to be 23,047 and 52% (26/50) of the groups were categorized as support. Conclusions: Melanoma pages and groups on Facebook reach a large portion of the population. To provide resources for the population of patients diagnosed with malignant melanoma and ensure that proper information is distributed, physicians and health care organizations may consider using Facebook as a platform to support and educate patients with melanoma.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".