Bibliometric Map Traces Computational Social Science From Big Data to Generative AI

Computational social science, the interdisciplinary field that brings computational methods to social science questions, has grown steadily over the past fifteen years, according to a new bibliometric analysis published in Information Systems Frontiers. The study, authored by Rukiye Ayaz, Hakan Özköse and Muhammet Berigel, maps the intellectual structure and thematic evolution of the field using 861 articles indexed in Scopus and published between 2011 and 2025.

The analysis combined two widely used bibliometric tools, VOSviewer and Biblioshiny, in a hybrid approach designed to reveal both the conceptual structure of the literature and the way its dominant themes have shifted over time. The results point to sustained expansion: annual output rose from just nine articles in 2011 to 126 in 2025, corresponding to an average annual growth rate of 20.74 percent.

Geographically, the field’s leading contributors were the United States, the United Kingdom and Germany. The core publication outlets identified in the analysis were PNAS, EPJ Data Science and Social Science Computer Review, the journals that carried the largest share of the field’s indexed research.

Keyword co-occurrence analysis identified five interconnected research clusters within the literature. These clusters are linked through three shared methodological hubs: social media, machine learning and natural language processing. According to the authors, this pattern of interconnection indicates a cohesive intellectual structure, in which different research streams draw on a common methodological core rather than developing in isolation.

The thematic evolution analysis traced three developmental phases in the field’s history.

The third and most recent phase is characterized by large language models and generative artificial intelligence, marking the field’s current maturity stage. The authors argue that these phases demonstrate that computational social science advances cumulatively, integrating emerging methodologies with established approaches rather than replacing them outright.

The study contributes a comprehensive overview of the field’s development and highlights its growing relevance to contemporary societal challenges. As a bibliometric analysis, it is based on Scopus-indexed articles and reflects the coverage of that database; the full findings appear in the journal’s 2026 volume, published online on 24 October 2026.

Subject of Research: A bibliometric mapping of the intellectual structure and thematic evolution of computational social science research from 2011 to 2025.

Article Title: From Big Data to Generative Artificial Intelligence: A Bibliometric Analysis of the Conceptual Structure and Thematic Evolution of Computational Social Science (2011–2025)

Article References: Ayaz, R., Özköse, H., & Beri̇gel, M. (2026). From Big Data to Generative Artificial Intelligence: A Bibliometric Analysis of the Conceptual Structure and Thematic Evolution of Computational Social Science (2011–2025). Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10830-3

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10830-3

Keywords: computational social science, bibliometrics, VOSviewer, Biblioshiny, Scopus, machine learning, natural language processing, social media, agent-based modelling, generative artificial intelligence, large language models, network analysis