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Journal article
Bibliometric Analysis on ChatGPT Research with CiteSpace
Information, v 16(1), 38
09 Jan 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
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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Details
- Title
- Bibliometric Analysis on ChatGPT Research with CiteSpace
- Creators
- Chaomei Chen (Corresponding Author) - Drexel University, Information ScienceDongyan NanXiangying ZhaoSeungjong Sun - Sungkyunkwan UniversityKyeo Re Lee - Hanyang University
- Publication Details
- Information, v 16(1), 38
- Publisher
- MDPI
- Number of pages
- 13
- Grant note
- SKKU Global Research Platform Research Fund, Sungkyunkwan University
This paper was supported by SKKU Global Research Platform Research Fund, Sungkyunkwan University, 2022-2024.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001404331600001
- Scopus ID
- 2-s2.0-85215756178
- Other Identifier
- 991022017598704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems