Social Media Engagement for Urology Journals — A Correlation Analysis of Traditional and Social Media Metrics
Bibliographic record
Abstract
IntroductionThe growing adoption of social media (SoMe) by the scientific community has cemented the role of SoMe in information dissemination and engagement of academic work. The objective of this study is to evaluate the relationship between traditional and alternative SoMe metrics of urology journals.MethodsUrology journals listed on the SCImago Journal & Country Rank (SJR) electronic portal were selected and data pertaining to traditional metrics were collected. Official SoMe platforms of eligible journals were identified and indicators of online activity were recorded. Correlations between traditional metrics (SJR, h-index, and Scopus CiteScore) and social metrics were performed via Spearman rank-order correlation.ResultsOf 107 journals, 54.2% of journals had at least one form of SoMe presence. The median SJR (0.535 versus 0.334, P = 0.005), h-index (34 versus 20, P = 0.001), and Scopus CiteScore (3.25 versus 2.20, P = 0.014) were significantly higher among journals with SoMe networks. All 3 traditional indicators demonstrated strong global correlations with various Twitter-based metrics (rs = 0.428 to 0.571). In particular, SoMe journals with more than 3000 citations in the previous 3 years also displayed very strong correlations between all 3 traditional metrics and alternative social metrics (rs = 0.714 to 0.821).ConclusionsJournals with SoMe presence had significantly higher traditional metric values (SJR, h-index, and CiteScore) compared to journals without SoMe presence. Strong, positive correlations between citation-based and alternative social metrics were also observed. Alternative social metrics may be harnessed as supplemental indicators of a journal’s scientific impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".