Annual Review of Sociology· 2019Q1· Review
Machine Learning for Sociology
- 271citations
- Q1SCImago
- 2019year
Short summary
Machine learning tools can address long-standing sociological questions about the limits of linear modeling, empirical evaluation criteria, and the context of discovery.
AI-generated from the title and abstract; the full text is not read.
Key points
- Machine learning algorithms extract information and knowledge from data.
- ML applications in sociology include distilling measures from new data sources (text, images), characterizing heterogeneity, improving causal inference, and aiding predictions.
- ML tools can address fundamental sociological questions regarding linear modeling limitations, empirical evaluation, and the context of discovery.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition to serving similar purposes in sociology, machine learning tools can speak to long-standing questions on the limitations of the linear modeling framework, the criteria for evaluating empirical findings, transparency around the context of discovery, and the epistemological core of the discipline.
The authors' abstract, as published at the source. Annual Review of Sociology, 2019 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences