Visualization and Bibliometric Analysis of Research Evolution on Metaverse
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of metaverse research, examining trends, academic output, and collaborations. A total of 1,200 papers were retrieved from web of science and analyzed using CiteSpace, VOSviewer, and Biblioshiny. Research on the metaverse has surged since 2022, driven by commercial interest. Leading institutions include the Chinese Academy of Sciences and Nanyang Technological University, with China and the USA dominating country collaborations. Keyword analysis highlights ‘virtual reality’ and ‘augmented reality,’ while ‘virtual worlds’ and ‘Second Life’ show notable bursts. Author networks reveal strong collaborations, though male researchers predominate, with Paola Paoloni standing out among female contributors. High-impact journals like IEEE Access and Sustainability frequently publish in this field, which is largely interdisciplinary, spanning computer science and engineering. This study, among the first of its kind, provides insights into the evolving landscape of metaverse research, guiding future studies and identifying unexplored areas for further exploration.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.174 | 0.190 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".