Bibliometric analysis of document flow on academic social networks in Web of Science
Bibliographic record
Abstract
Abstract Analysis of a document array on academic social networks (ASNs) in Web of Science for the period from 2005 to 2020 was carried out with use of analytical services data of the WoS and CiteSpace (the program for visualization of patterns and trends in scientific literature). The following parameters of the array were analyzed: publication dynamics; document types structure; countries, organizations and authors leading in the number of publications; thematic categories to which documents of the array are assigned; publications (journals, monographs) in which the documents of the array are published; most cited publications. An increase in the number of publications on the ASNs in WoS was established since 2005. The largest number of ASNs studies is conducted in the USA (University of Pittsburgh), UK (Wolverhampton University, Manchester University), China, Spain (University of Granada), Germany (Max Planck Society for Scientific Research), Canada, India and the Netherlands (Leiden University). The ASNs were studied in the main thematic areas: Computer Science, Computer Science and Librarianship, Mechanical Engineering, Engineering and Technology. Four out of the first ten highly cited publications, are devoted to altmetrics. Using the CiteSpace, it was shown that when ASNs started rise, their organizational structure was beeing studed. Later, altmetrics used in the ASNs became the main subjects of ASNs research. The keywords occurrence revealed that the most frequent terms are “altmetrics”, “impact”, “citation”. As part of the document flow, also identified publications in which the ASNs are used as a source of bibliographic data for systematic or meta-analysis (in medicine predominantly), or as a platform for experimental data discussion.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.073 | 0.071 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".