Machine Learning in Social Science
Description
This open access book explores how machine learning can enhance both quantitative and qualitative research in sociology. By developing algorithms tailored to specific data, machine learning enables social scientists to uncover patterns, generate new theories, calibrate indicators, and strengthen causal inference. The book offers an accessible introduction to the principles and applications of supervised and unsupervised learning (Part I), followed by empirical case studies across key areas of sociological research. In the social prediction section (Parts II-IV), it illustrates how supervised learning can 1) impute missing indicators, 2) derive theories directly from data, and 3) improve causal inference through counterfactual construction. In the culture modeling section (Parts V-VI), it shows how unsupervised machine learning can map the structure of large-scale cultural texts-such as online novels and film databases-making complex cultural patterns visible across time and space.
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Persons
Yunsong Chen is Changjiang Distinguished Professor of sociology at the Department of Sociology, Nanjing University. He earned a D.Phil. in sociology from University of Oxford, Nuffield College.
Zhuo Chen is Postdoctoral Research Fellow in sociology at the Department of Sociology, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Wen Ma is Research Associate at the School of Journalism and Communication, Nanjing University. She earned a Ph.D. in sociology from Nanjing University.
Guodong Ju is Postdoctoral Research Fellow in social attitudes at the China Institute, University of Alberta. He earned a Ph.D. from London School of Economics and Political Science (LSE).
Content
Chapter 1: Introduction: The Rise of Machine Learning in Social Science.- Part I: Basics of Machine Learning for Social Science.- Chapter 2: Social Prediction: A New Research Paradigm Based on Supervised Machine Learning.- Chapter 3: Modeling Massive: Discovering Structure using Unsupervised Machine Learning.- Part II: Measuring the Unmeasurable.- Chapter 4: Unspeakable Violence: Predicting the Incidence of Intimate Partner Violence.- Chapter 5: Hidden Identities: Predicting Sexual Minority Orientation among Youth.- Part III: Developing Theories.- Chapter 6: Computing Grounded Theory: Algorithmic Approaches to Theory Construction.- Chapter 7: Applications of Computing Grounded Theory: Revisiting Subjective Well-being.- Part IV: Identifying Causality.- Chapter 8: Enhancing Traditional Methods of Causal Inference using Machine Learning: Optimizing Matching, Instrumental Variables, and Quasi-Experiments.- Chapter 9: Double Machine Learning for Causal Inference on High-Dimensional Data: A Flexible and Robust Approach to Causal Estimation.- Part V: Modeling Topics.- Chapter 10: Modeling Gender Consciousness: Women Authors' Creation in Boys' Love Fiction.- Chapter 11: Modeling Ideology: A Distant Reading of Marx/Engels Collected Works .- Part VI: Modeling Sentiments.- Chapter 12: From Barbarism to Civilization: The Evolving Image of China in International Cinema.- Chapter 13: A United Tale of Three Regions: A Century of China on Screen.