Social Media Network Analysis of Academic Urologists’ Interaction Within Twitter Microblogging Environment
Bibliographic record
Abstract
ObjectiveTo characterize academic urology Twitter presence and interaction by subspecialty designation.MethodsUsing Twitter application programming interface of available data, 94000 specific tweets were extracted for the analysis through the Twitter Developer Program. Academic urologists were defined based on American Urological Association (AUA) residency program registration of 143 residency programs, with a total of 2377 faculty. Two of 3-factor verification (name, location, specialty) of faculty Twitter account was used. Additional faculty information including sex, program location, and subspecialty were manually recorded. All elements of microblogging were captured through Anaconda Navigator. Analyzed tweets were further evaluated using natural language processing for sentiment association, mentions, and quote tweeted and retweeted. Network analysis based on interactions of academic urologist within specialty for given topic were analyzed using D3 in JavaScript. Analysis was performed in Python and R.ResultsWe identified 143 residency programs with a total of 2377 faculty (1975 men and 402 women). Among all faculty, 945 (39.7%) had registered Twitter accounts, with the majority being men (759 [80.40%] versus 185 [19.60%]). Although there were more male academic urologists across programs, women within academic urology were more likely to have a registered Twitter account overall (46% versus 38.5%) compared with men. When assessing registered accounts by sex, there was a peak for male faculty in 2014 (10.05% of all accounts registered) and peak for female faculty in 2015 (2.65%). There was no notable change in faculty account registration during COVID-19 (2019–2020). In 2022, oncology represented the highest total number of registered Twitter users (225), with the highest number of total tweets (24622), followers (138541), and tweets per user per day (0.32). However, andrology (50%) and reconstruction (51.3%) were 2 of the highest proportionally represented subspecialties within academic urology. Within the context of conversation surrounding a specified topic (#aua21), female pelvic medicine and reconstructive surgery (FPMRS) and endourology demonstrated the total highest number of intersubspecialty conversations.ConclusionsThere is a steady increase in Twitter representation among academic urologists, largely unaffected by COVID-19. While urologic oncology represents the largest group, andrology and reconstructive urology represent the highest proportion of their respective subspecialties. Interaction analysis highlights the variant interaction among subspecialties based on topic, with strong direct ties between endourology, FPMRS, and oncology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".