Computational Social Science Discovery and Prediction [Paperback]

This book provides an overview of cutting-edge approaches to computational social science.This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior.This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior.Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.Preface Gary King; Introduction R. Michael Alvarez; Part I. Computation Social Science Tools: 1. The application of big data in surveys to the study of public opinion, elections, and representation Christopher Warshaw; 2. Navigating the local modes of big data: the case of topic models Margaret Roberts, Brandon Stewart and Dustin Tingley; 3. Generating political event data in near realƒœ