What is it about?
This paper conducts a large-scale quantitative examination (on 11 million papers) of how computational social science emerges from - and transforms - the social sciences. We use machine learning to show how it first formed a distinct cluster, established boundaries, and later diffused back into the social sciences - ultimately unifying them.
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Why is it important?
We provide the first large-scale, data-driven map of how computational social science emerged and reshaped the social sciences over 30 years. This work offers a timely lens on how new fields gain influence and transform academic knowledge - especially relevant in an era of rapidly evolving AI-driven research.
Perspectives
Working on this paper was both intellectually challenging and deeply rewarding. It brought together our interest in how new ideas spread with cutting-edge tools from machine learning and computational social science. We were especially fascinated to see how an emerging field could both divide and then reconnect long-standing disciplines. We hope this study encourages others to look beyond traditional academic boundaries and to think more critically about how new methods and technologies reshape what we study - and how we study it.
Honglin Bao
University of Chicago
Read the Original
This page is a summary of: From Division to Unity: A Large-Scale Study on the Emergence of Computational Social Science, 1990-2021, May 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3701716.3715502.
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