Machine Learning in Social Science - Applications and Advances

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.

List of contents

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:

Product details

• Authors: Yunsong Chen, Zhuo Chen, Guodong Ju, Wen Ma, Wen et al Ma
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 19.05.2026
• EAN: 9789819564644
• ISBN: 978-981-9564-64-4
• Illustrations: Approx. 350 p.
• Subjects: Humanities, art, music > Linguistics and literary studies > General and comparative literary studies; Soziologie, Kulturwissenschaften, Künstliche Intelligenz, Sentiment Analysis, Cultural Studies, Sociology of Culture, Artificial Intelligence, Text Mining, Open Access, Computer-Anwendungen in Kunst und Geisteswissenschaften, Digital Humanities, Topic Modeling, social prediction, causal inference using machine learning, Computational sociology