Guidelines for optimal utilization of social media for brain tumor stakeholders
Bibliographic record
Abstract
OBJECTIVE: Effective use of social media (SM) by medical professionals is vital for better connections with patients and dissemination of evidence-based information. A study of SM utilization by different stakeholders in the brain tumor community may help determine guidelines for optimal use. METHODS: Facebook, Twitter, and YouTube were searched by using the term "Brain Tumor." Platform-specific metrics were determined, including audience size, as a measure of popularity, and mean annual increase in audience size, as a measure of performance on SM. Accounts were categorized on the basis of apparent ownership and content, with as many as two qualitative themes assigned to each account. Correlations of content themes and posting behavior with popularity and performance metrics were assessed by using the Pearson's test. RESULTS: Facebook (67 pages and 304,581 likes) was predominantly used by organizations (64% of pages). Top themes on Facebook, Twitter, and YouTube were charity and fundraising (67% of pages), education and research (72% of accounts), and experience sharing and support seeking (48% of videos, 60% of views, and 82% of user engagement), respectively. On Facebook, only the presence of other concurrent platforms influenced a page's performance (rho = 0.59) and popularity (rho = 0.61) (p < 0.05). On Twitter, the number of monthly tweets (rho = 0.66) and media utilization (rho = 0.78) were significantly correlated with increased popularity and performance (both p < 0.05). Personal YouTube videos (30% of videos and 61% of views) with the theme of experience sharing and support seeking had the highest level of engagement (60% of views, 70% of comments, and 87% of likes). CONCLUSIONS: Popularity and prevalence of qualitative themes differ among SM platforms. Thus, optimal audience engagement on each platform can be achieved with thematic considerations. Such considerations, along with optimal SM behavior such as media utilization and multiplatform presence, may help increase content popularity and thus increase community access to neurooncology content provided by medical professionals.
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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 | 0.060 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".