A Multidisciplinary Bibliometric Analysis of Differences and Commonalities Between GenAI in Science

Kacper Sieciński and Marian Oliński ()
Additional contact information
Kacper Sieciński: Faculty of Economic Sciences, Institute of Management and Quality Sciences, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
Marian Oliński: Faculty of Economic Sciences, Institute of Management and Quality Sciences, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland

Publications, 2025, vol. 13, issue 4, 1-29

Abstract: Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top–50, Top–100, and Top–200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities.

Keywords: generative AI; large language models; bibliometrics; keyword co-occurrence; Jaccard similarity; co-authorship networks; scientific communication; Web of Science (search for similar items in EconPapers)
JEL-codes: A2 D83 L82 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2304-6775/13/4/67/pdf (application/pdf)
https://www.mdpi.com/2304-6775/13/4/67/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jpubli:v:13:y:2025:i:4:p:67-:d:1815568

Access Statistics for this article

Publications is currently edited by Ms. Jennifer Zhang

More articles in Publications from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().