Bibliometric Insights Into the Infodemic: Global Research Trends and Policy Responses: Quantitative Research
Bibliographic record
Abstract
Background: Amidst the COVID-19 pandemic, the proliferation of misinformation on social media, termed the "infodemic," has complicated global health responses. Objective: This study aims to identify research trends and information-making in the context of this challenge. This paper synthesizes key areas of scholarly investigation into the COVID-19 infodemic, both within China and internationally, to guide public health strategies and the management of public sentiment. Methods: By employing a bibliometric approach, using CiteSpace software, we conducted a visual analysis of the global literature, covering a total of 1437 publications from the Web of Science and the China National Knowledge Infrastructure core databases between 2016 and 2025, focusing on publication trends, citation frequencies, and keyword clusters. Results: After analysis, the results reveal distinct focal points in the research priorities of Chinese and international scholars. International studies often focus on machine learning and public psychology, while Chinese research tends to address information control and safeguarding. Common ground is found in the interest in preventing the spread of misinformation. While literature on COVID-19 abounds, cross-national systematic reviews are limited. Conclusions: This paper fills this gap through a comparative bibliometric analysis, offering valuable insights for information management, media communication, and public administration, thus charting new directions for future research.
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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 | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.031 | 0.157 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, 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".