Bibliometric Analysis on ChatGPT Research with CiteSpace

Dongyan Nan; Xiangying Zhao; Seungjong Sun; Kyeo Re Lee; Jang Hyun Kim

doi:10.3390/info16010038

journal article Open Access Jan 09, 2025

Bibliometric Analysis on ChatGPT Research with CiteSpace

Dongyan Nan

Xiangying Zhao Seungjong Sun Kyeo Re Lee

Jang Hyun Kim

Information Vol. 16 No. 1 pp. 38 · MDPI AG

View at Publisher Save 10.3390/info16010038

Abstract

ChatGPT is a generative artificial intelligence (AI) based chatbot developed by OpenAI and has attracted great attention since its launch in late 2022. This study aims to provide an overview of ChatGPT research through a CiteSpace-based bibliometric analysis. We collected 2465 published articles related to ChatGPT from the Web of Science. The main forces in ChatGPT research were identified by examining productive researchers, institutions, and countries/regions. Moreover, we performed co-authorship network analysis at the levels of author and country/region. Additionally, we conducted a co-citation analysis to identify impactful researchers, journals/sources, and literature in the ChatGPT field and performed a cluster analysis to identify the primary themes in this field. The key findings of this study are as follows. First, we found that the most productive researcher, institution, and country in ChatGPT research are Ishith Seth/Himel Mondal, Stanford University, and the United States, respectively. Second, highly cited researchers in this field are Tiffany H. Kung, Tom Brown, and Malik Sallam. Third, impactable sources/journals in this area are ARXIV, Nature, and Cureus Journal of Medical Science. Fourth, the most impactful work was published by Kung et al., who demonstrated that ChatGPT can potentially support medical education. Fifth, the overall author-based collaboration network consists of several isolated sub-networks, which indicates that the authors work in small groups and lack communication. Sixth, United Kingdom, India, and Spain had a high degree of betweenness centrality, which means that they play significant roles in the country/region-based collaboration network. Seventh, the major themes in the ChatGPT area were “data processing using ChatGPT”, “exploring user behavioral intention of ChatGPT”, and “applying ChatGPT for differential diagnosis”. Overall, we believe that our findings will help scholars and stakeholders understand the academic development of ChatGPT.

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References

Details

Published: Jan 09, 2025
Vol/Issue: 16(1)
Pages: 38
License: View

Authors

D

Dongyan Nan

School of Business, Macau University of Science and Technology, Macau 999078, China

X

Xiangying Zhao

Department of Interaction Science, Sungkyunkwan University, Seoul 03063, Republic of Korea

S

Seungjong Sun

Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul 03063, Republic of Korea; Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03063, Republic of Korea

K

Kyeo Re Lee

Center for Creative Convergence Education, Hanyang University ERICA, Ansan 15588, Republic of Korea

J

Jang Hyun Kim

Department of Interaction Science, Sungkyunkwan University, Seoul 03063, Republic of Korea; Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul 03063, Republic of Korea; Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03063, Republic of Korea

Funding

SKKU Global Research Platform Research Fund, Sungkyunkwan University Award: 2022–2024

Cite This Article

Dongyan Nan, Xiangying Zhao, Seungjong Sun, et al. (2025). Bibliometric Analysis on ChatGPT Research with CiteSpace. Information, 16(1), 38. https://doi.org/10.3390/info16010038

Bibliometric Analysis on ChatGPT Research with CiteSpace

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