The Resource Computational social science : discovery and prediction, R. Michael Alvarez
Computational social science : discovery and prediction, R. Michael Alvarez
Resource Information
The item Computational social science : discovery and prediction, R. Michael Alvarez represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Computational social science : discovery and prediction, R. Michael Alvarez represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- 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.--
- Language
- eng
- Extent
- x, 327 pages
- 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
- Isbn
- 9781107107885
- Label
- Computational social science : discovery and prediction
- Title
- Computational social science
- Title remainder
- discovery and prediction
- Statement of responsibility
- R. Michael Alvarez
- 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.--
- 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
- 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
- 9781107107885
- 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
- 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
- 9781107107885
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- System control number
- (OCoLC)946546003
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Computational-social-science--discovery-and/9Y1YpArXX3U/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Computational-social-science--discovery-and/9Y1YpArXX3U/">Computational social science : discovery and prediction, R. Michael Alvarez</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
