A Portrait of Current Radiation Oncology Twitter Influencers
Bibliographic record
Abstract
Introduction We aimed to characterize the most influential radiation oncologists on Twitter, the correlation between their Twitter activity and their academic profiles as measured by the Scopus H-index as well as their activity around the American Society for Radiation Oncologists (ASTRO) 2018 meeting. Methods We defined radiation oncology influencers as any radiation oncologist with 500 or more followers on Twitter through the first two weeks of August 2019. We collected their available characteristics, their Scopus H-index, and Twitter metrics. We examined their general Twitter activity as well as their specific activity before, during, and after the 2018 ASTRO annual meeting. We identified the most frequent tweet content categories for each influencer. Results We identified 48 radiation oncologist influencers; 79% were male, 75% were based in the United States, and 94% were affiliated with an academic center. Among them, 44% had high H-indices of ≥21, an average value in academic faculty for full professors or department heads. There were no correlations between H-index and Twitter metrics such as the number of individuals the influencer was following (p = 0.58), the number of followers (p = 0.66), the number of tweets (p = 0.88), and the number of likes (p = 0.54). During the period around ASTRO 2018, the mean number of tweets per influencer was 4437 (range 87-93,000). Conclusion Current radiation oncology influencers are predominantly North American males from academic institutions. A correlation between academic productivity as measured by the H-index and Twitter metrics was not demonstrated. The fact that some influencers had a low H-index supports that a high academic profile as measured by traditional metrics is not necessary to have a voice in the Twitter radiation oncology community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".