Computational social science: Obstacles and opportunities

D. M. J. Lazer, A. Pentland, et al (2020). Computational social science: Obstacles and opportunities, Science, 369 (6507), pp. 1060-1062, DOI: 10.1126/science.aaz8170


The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.

We define CSS as the development and application of computational methods to complex, typically large-scale, human (sometimes simulated) behavioral data (1). Its intellectual antecedents include research on spatial data, social networks, and human coding of text and images. Whereas traditional quantitative social science has focused on rows of cases and columns of variables, typically with assumptions of independence among observations, CSS encompasses language, location and movement, networks, images, and video, with the application of statistical models that capture multifarious dependencies within data. A loosely connected intellectual community of social scientists, computer scientists, statistical physicists, and others has coalesced under this umbrella phrase.

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