The Dramatic Increase in Social Media in Urology
Bibliographic record
Abstract
PURPOSE: Social media are established tools for rapid information dissemination to a broad audience. A major use has been the compilation of conference specific messaging known as tweets via preselected hashtags on Twitter. We analyzed Twitter use between consecutive years at the AUA (American Urological Association) and CUA (Canadian Urological Association) annual meetings. MATERIALS AND METHODS: Publicly available tweets containing the established meeting hashtags were abstracted from an online archive. Tweets were categorized by author type and by content as informative (based on research presented at the conference) or uninformative (unrelated to presented data) according to an established classification scheme. RESULTS: We analyzed 5,402 tweets during the combined 18 meeting days, of which 4,098 were original and 1,304 were rebroadcast prior tweets. There was a large increase in Twitter use at the 2013 annual meetings compared to the 2012 meetings (4,591 tweets from a total of 540 accounts vs 811 from 134). Biotechnology analysts represented the highest volume of tweets (226 or 28%) in 2012 but in 2013 this majority shifted to urologists (2,765 or 60%). Of the tweets 29% were categorized as informative in 2012, which increased to 41% at the 2013 meetings. CONCLUSIONS: Twitter has emerged as a significant communication platform at urological meetings. Use increased dramatically between 2012 and 2013. Urologists have increasingly led this discussion with an increased focus on data arising from meeting proceedings. This adjunct to traditional meeting activity merits the attention of urologists and the professional associations that host such meetings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".