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The Resource Computational social science : discovery and prediction, R. Michael Alvarez

Computational social science : discovery and prediction, R. Michael Alvarez

Label
Computational social science : discovery and prediction
Title
Computational social science
Title remainder
discovery and prediction
Statement of responsibility
R. Michael Alvarez
Creator
Editor
Subject
Language
eng
Summary
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.--
Member of
Assigning source
Provided by publisher
Cataloging source
DLC
http://library.link/vocab/creatorDate
1964-
http://library.link/vocab/creatorName
Alvarez, R. Michael
Index
index present
Literary form
non fiction
Nature of contents
bibliography
Series statement
Analytical methods for social research
http://library.link/vocab/subjectName
  • Social sciences
  • Social sciences
  • Social sciences
Label
Computational social science : discovery and prediction, R. Michael Alvarez
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Contents
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 12. Computational social science: towards a collaborative future Hanna Wallach
Control code
FIEb17833309
Dimensions
23 cm.
Extent
x, 327 pages
Isbn
9781107518414
Media category
unmediated
Media MARC source
rdamedia.
Media type code
  • n
System control number
(OCoLC)946546003
Label
Computational social science : discovery and prediction, R. Michael Alvarez
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Contents
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 12. Computational social science: towards a collaborative future Hanna Wallach
Control code
FIEb17833309
Dimensions
23 cm.
Extent
x, 327 pages
Isbn
9781107518414
Media category
unmediated
Media MARC source
rdamedia.
Media type code
  • n
System control number
(OCoLC)946546003

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