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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".