Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13193062

Authors: Hua Li, Qifang Wang, Ye Wu

Institutions: Beijing Normal University, Beijing Normal University, Zhuhai

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Fundamental Research Funds for the Central Universities