Visibility, impact, and applications of bibliometric software tools through citation analysis

Background: Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach. Methods: The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals. Results: The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals. Conclusions: This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.

The bibliometric study based on the Scopus database aims to investigate the global literary development of “decent work” over a span exceeding two decades. The noticeable surge in publications since 2015 underscores the growing significance of the concept. Limited representation of lower‐middle and lower‐income countries, except India, among the top 10 contributors is observed, revealing a considerable research gap in the study of decent work. The research highlights five principal themes: the labor market's impact on economic development, corporate social responsibility and sustainable development, gender equality, governance, and social justice, as well as decent work, job quality, education, well‐being, and job satisfaction. The study also shows the importance of the topic in different fields. Future researchers may consider the variables or indicators identified for surveys and questionnaire construction.

Scholarly interest in the emerging topic of multimodal learning and its ethical considerations, which integrates a variety of informational forms to enhance learning, has significantly increased during the past ten years. This paper aims to pinpoint the conceptual foundations and development trend of multimodal learning through thorough bibliometric research. By examining patterns, trends, and linkages within the published literature using a sizable collection of articles from the Scopus database, this research attempts to map the scientific landscape of multimodal learning. According to our study, multimodal learning is becoming more popular in a variety of academic disciplines. The major works and new trends in the subject were highlighted by emphasizing key publications and significant authors. While developing trends and recurrent study subjects were determined using keyword analysis, the major papers having the greatest influence on the field were found utilizing co-citation analysis. The collaborative network analysis also revealed a vibrant academic community with expanding global ties, fostering an atmosphere that is conducive to cutting-edge multimodal learning research. With its insights on the past, present, and likely future paths of multimodal learning, this study is an invaluable resource for academics, teachers, and decision-makers.