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

Author

Listed:
  • Hua Li

    (School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
    Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, China)

  • Qifang Wang

    (School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
    Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, China)

  • Ye Wu

    (School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
    Center for Computational Communication Research, Beijing Normal University, Zhuhai 519087, China)

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.

Suggested Citation

  • Hua Li & Qifang Wang & Ye Wu, 2025. "From Mobile Media to Generative AI: The Evolutionary Logic of Computational Social Science Across Data, Methods, and Theory," Mathematics, MDPI, vol. 13(19), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3062-:d:1756138
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