MAPPING THE EMERGING FIELD OF COMPUTATIONAL SOCIAL SCIENCE: PARADIGMATIC DIMENSIONS AND INTELLECTUAL STRUCTURE

Abstract

The digitalization of social life has profoundly reshaped the production and circulation of information, giving rise to an unprecedented abundance of digital traces. In this context, Computational Social Science (CSS) has emerged as an interdisciplinary field seeking to explain social phenomena through computational methods and large-scale data analysis. This study investigates whether CSS represents a genuine theoretical-methodological transformation within the social sciences or remains primarily an instrumental set of analytical techniques. A mixed-method design combined a bibliometric analysis of 100 documents (1997–2025) indexed in Scopus and Web of Science with a scoping review of 14 highly cited foundational works. Data were processed in R using the bibliometrix package and visualized in Gephi to map thematic and intellectual structures. Findings reveal rapid institutional consolidation of the field after Lazer (2009), concentrated in elite institutions in the United States, China, and Europe. The thematic network is dominated by big data and machine learning, while classical social theory occupies peripheral positions. The most influential literature remains largely meta-scientific and programmatic, focusing on defining the field rather than empirically applying its methods. CSS currently stands at a crossroads between data-driven methodology and theoretically informed social explanation. Its maturity depends on integrating computational innovation with robust social theory and expanding beyond Global North infrastructures. This synthesis may redefine how social sciences generate and validate knowledge in the digital age.