Qualitative Twitter analysis of participants, tweet strategies, and tweet content at a major urologic conference
Bibliographic record
Abstract
INTRODUCTION: The microblogging social media platform Twitter is increasingly being adopted in the urologic field. We aimed to analyze participants, tweet strategies, and tweet content of the Twitter discussion at a urologic conference. METHODS: A comprehensive analysis of the Twitter activity at the European Association of Urology Congress 2013 (#eau2013) was performed, including characteristics of user profiles, engagement and popularity measurements, characteristics and timing of tweets, and content analysis. RESULTS: Of 218 Twitter contributors, doctors (45%) were the most frequent, ahead of associations (15%), companies (10%), and journals (3%). However, journals had the highest tweet/participant rate (22 tweets/participant), profile activity (median: 1177, total tweets, 1805 followers, 979 following), and profile popularity (follower/following ratio: 2.1; retweet rank percentile: 96%). Links in a profile were associated with higher engagement (p<0.0001) and popularity (p<0.0001). Of 1572 tweets, 57% were original tweets, 71% contained mentions, 20% contained links, and 25% included pictures. The majority of tweets (88%) were during conference hours, with an average of 24.7 tweets/hour and a peak activity of 71 tweets/hour. Overall, 59% of tweets were informative, led by the topics uro-oncology (21%), urologic research (21%), and urotechnology (12%). Limitations include the analysis of a single conference analysis, assessment of global profile and not domain-specific activity, and the rapid evolution in Twitter-using habits. CONCLUSION: Results of this single conference qualitative analysis are promising for an enrichment of the scientific discussions at urologic conferences through the use of Twitter.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".