Analyzing the role of generative AI in social sciences: A bibliometric and thematic study

Abstract

The study aims to explore the impact and trends of generative artificial intelligence (GenAI)leveraging within the field of social sciences. The primary objective is identifying key themes,influential articles, and emerging trends in applying GenAI technologies in social science research.Methods: The research employs a two-fold methodology. First, a bibliometric analysis is conductedusing Scopus, Web of Science, and ScienceDirect databases to gather relevant publications from 2023to 2025. This quantitative analysis identifies the most influential articles, authors, journals, andcountries contributing to the field among 1223 scientific articles from the last three years. VOSvieweris used to visualize and analyse citation networks. Second, a qualitative thematic analysis isperformed on the most influential 118 articles identified in the bibliometric analysis. This involves adetailed content review to extract and categorize key themes and concepts. The thematic analysisframework helps understand the various dimensions through which GenAI is studied and applied inthe social sciences.Findings: The bibliometric analysis reveals that the most influential articles and research areconcentrated in a few leading journals and authored by prominent researchers. The thematic analysisidentifies several key themes, including GenAI opportunities and challenges in higher education,ethical implications, and risks associated with applying these new technologies. Technological themesfocus on the advancements and applications of AI technologies, while ethical themes addressconcerns related to privacy, bias, and the societal impact of AI.Overall, the study highlights the growing importance of GenAI in social science research and providesa comprehensive overview of the field's current state. It also suggests future research directions toaddress gaps and challenges identified in the analysis.

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