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Computational Social Science 2016

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R. Michael Alvarez:
Computational Social Science: Discovery and Prediction. Analytical Methods for Social Research, Cambridge University Press 2016, ISBN 9781316257340
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Gary King:
Preface. vii-x
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R. Michael Alvarez:
Introduction. 1-24

Part 1 - Computational Social Science Tools

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Christopher Warshaw:
The Application of Big Data in Surveys to the Study of Elections, Public Opinion, and Representation. 27-50
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Margaret E. Roberts, Brandon M. Stewart, Dustin Tingley:
Navigating the Local Modes of Big Data: The Case of Topic Models. 51-97
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John Beieler, Patrick T. Brandt, Andrew Halterman, Philip A. Schrodt, Erin M. Simpson:
Generating Political Event Data in Near Real Time: Opportunities and Challenges. 98-120
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Betsy Sinclair:
Network Structure and Social Outcomes: Network Analysis for Social Science. 121-139
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Peter Foley:
Ideological Salience in Multiple Dimensions. 140-167
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Daniel Conn, Christina M. Ramirez:
Random Forests and Fuzzy Forests in Biomedical Research. 168-196

PART 2 - Computational Social Science Applications

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Joshua A. Tucker, Jonathan Nagler, Megan Macduffee Metzger, Pablo Barberá, Duncan Penfold-Brown, Richard Bonneau:
Big Data, Social Media, and Protest: Foundations for a Research Agenda. 199-224
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Justin Grimmer:
Measuring Representational Style in the House: The Tea Party, Obama, and Legislators' Changing Expressed Priorities. 225-245
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Brian Griepentrog, Sean Marsh, Sidney Carl Turner, Sarah Evans:
Using Social Marketing and Data Science to Make Government Smarter. 246-265
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Inés Levin, Julia Pomares, R. Michael Alvarez:
Using Machine Learning Algorithms to Detect Election Fraud. 266-294
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Phillip N. Price, Andrew Gelman:
Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience. 295-306
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Hanna M. Wallach:
Conclusion - Computational Social Science: Toward a Collaborative Future. 307-316

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