From Mobile Media to Generative AI: The Evolutionary Logic of Computational Social Science Across Data, Methods, and Theory

Hua Li, Qifang Wang and Ye Wu ()
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Hua Li: School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
Qifang Wang: School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
Ye Wu: School of Journalism and Communication, Beijing Normal University, Beijing 100875, China

Mathematics, 2025, vol. 13, issue 19, 1-17

Abstract: Since its articulation in 2009, Computational Social Science (CSS) has grown into a mature interdisciplinary paradigm, shaped first by mobile media-generated digital traces and more recently by generative AI. With over a decade of development, CSS has expanded its scope across data, methods, and theory: data sources have evolved from mobile traces to multimodal records; methods have diversified from surveys and experiments to agent-based modeling, network analysis, and computer vision; and theory has advanced by revisiting classical questions and modeling emergent digital phenomena. Generative AI further enhances CSS through scalable annotation, experimental design, and simulation, while raising challenges of validity, reproducibility, and ethics. The evolutionary logic of CSS lies in coupling theory, models, and data, balancing innovation with normative safeguards to build cumulative knowledge and support responsible digital governance.

Keywords: mathematical modeling; computational social science; generative AI; integration of theory and method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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