Computational social science: Obstacles and opportunities

By David M. J. Lazer, Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir S. Contractor, Deen Freelon, Sandra González-Bailón, Gary King, Helen Z. Margetts and 5 more. Science, 2020

365 citations recorded by the source corpus.

Data sharing, research ethics, and incentives must improve 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.

Subfields: General Social Sciences, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c1e49d830e67269d4d2053a5f124ea773c79b740

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Citations in the corpus

The generalizability crisis (2019, background): “In the long term, taking time to understand how behavioral, situational, and environmental/contextual factors can be extracted from objective digital data will allow psychology to develop robust contextualized and comprehensive theory (Lazer et al., 2020).”
Can Large Language Models Transform Computational Social Science? (2023, methodology): “Following Lazer et al. (2020), we define Computational Social Science as the development and application of computational methods to the scientific analysis of behavioral and linguistic data.”
A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use (2021, background): “Third, while statistical approaches cannot resolve all biases and sources of error, if research can identify factors that systematically account for discrepancies, they can be modelled and used to account for the misalignment between self-reported and logged measures of digital media use 92–94 .”