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 conducted using Scopus, Web of Science, and ScienceDirect databases to gather relevant publications from 2023 to 2025. This quantitative analysis identifies the most influential articles, authors, journals, and countries contributing to the field among 1223 scientific articles from the last three years. VOSviewer is used to visualize and analyse citation networks. Second, a qualitative thematic analysis is performed on the most influential 118 articles identified in the bibliometric analysis. This involves a detailed content review to extract and categorize key themes and concepts. The thematic analysis framework helps understand the various dimensions through which GenAI is studied and applied in the social sciences. Findings: The bibliometric analysis reveals that the most influential articles and research are concentrated in a few leading journals and authored by prominent researchers. The thematic analysis identifies several key themes, including GenAI opportunities and challenges in higher education, ethical implications, and risks associated with applying these new technologies. Technological themes focus on the advancements and applications of AI technologies, while ethical themes address concerns related to privacy, bias, and the societal impact of AI. Overall, the study highlights the growing importance of GenAI in social science research and provides a comprehensive overview of the field's current state. It also suggests future research directions to address gaps and challenges identified in the analysis.

Ähnliche Arbeiten