Visual Analysis of the Research Context of Opinion Leaders Based on CiteSpace
Bibliographic record
Abstract
Since 2000, an increasing number of studies of opinion leaders has analyzed their roles from different perspectives in a wide range of industries. However, few studies had attempted to comprehensively review the existing literature in the past. The purpose of this research is to map the thread and skeleton of the research on opinion leaders from 2000 to 2021. A total of 3,872 related articles have been collected from Wos for scientometrics analysis. The research results show that (1) The research literature on opinion leaders has been in the ascending stage all along, and scholars remain enthusiastic on this research. (2) The most significant contributions mainly come from the United States, Britain, Canada, China, and Australia, but showing no high cooperation intensity. (3) According to the keyword time zone view, it is found that, with the development of time, its influence is gradually reflected on the internet with the development of science and technology. (4) Nine research classifications are able to be drawn in cluster analysis. By using Citespace software, this study is to analyze the last 20-year literature on opinion leaders and sort out their development context, while the data used in this study is merely retrieved from the WoS core database. The boundaries of this study, in the future, could be expanded by considering other types of databases and documents so as to integrate a more comprehensive knowledge map of opinion leaders.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".