TY - JOUR
T1 - Bibliometric Analysis on ChatGPT Research with CiteSpace
AU - Nan, Dongyan
AU - Zhao, Xiangying
AU - Chen, Chaomei
AU - Sun, Seungjong
AU - Lee, Kyeo Re
AU - Kim, Jang Hyun
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - bibliometric analysis
KW - ChatGPT
KW - CiteSpace
KW - cluster analysis
KW - co-authorship analysis
KW - co-citation analysis
UR - https://www.scopus.com/pages/publications/85215756178
U2 - 10.3390/info16010038
DO - 10.3390/info16010038
M3 - Article
AN - SCOPUS:85215756178
SN - 2078-2489
VL - 16
JO - Information (Switzerland)
JF - Information (Switzerland)
IS - 1
M1 - 38
ER -
