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Updated: Jun 16, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric
Sam Yu-Chieh Ho1, Julie Chi Chow2,3, Willy Chou4,5
1Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Background:
The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period.
Methods:
Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm.
Results:
Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions.
Conclusion:
Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.
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