'With big data analytics comes a complex relationship between computational social science and public policy. For social scientists, these essays will present exciting new ways to think about and leverage big data analytics. Data scientists will enjoy seeing their tricks of the trade being applied to interesting social and public policy issues.' Jeff Jonas, IBM Fellow
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 real time: opportunities and challenges John Beieler, Patrick T. Brandt, Andrew Halterman, Philip A. Schrodt and Erin M. Simpson; 4. Network structure and social outcomes: network analysis for social science Betsy Sinclair; 5. Ideological salience in multiple dimensions Peter Foley; 6. Random forest applied to feature selection in biomedical research Daniel Conn and Christina Ramirez; Part II. Computation Social Science Applications: 7. Big data, social media, and protest: foundations for a research agenda Joshua Tucker, Jonathan Nagler, Megan Metzger, Pablo Barbera, Duncan Penfold-Brown, John Jost and Richard Bonneau; 8. Measuring representational style in the House: the Tea Party, Obama and legislators' changing expressed priorities Justin Grimmer; 9. Using social marketing and data science to make government smarter Brian Griepentrog, Sean Marsh, Sidney Carl Turner and Sarah Evans; 10. Using machine algorithms to detect election fraud Ines Levin, Julia Pomares and R. Michael Alvarez; 11. Centralized analysis of local data, with dollars and lives on the line: lessons from the home radon experience Phillip N. Price and Andrew Gelman; Conclusion. Computational social science: towards a collaborative future Hanna Wallach.