{"product_id":"computational-social-science-discovery-and-prediction-hardback-9781107107885","title":"Computational Social Science; Discovery and Prediction (Hardback) 9781107107885","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputational Social Science\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eDiscovery and Prediction\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eThis book provides an overview of cutting-edge approaches to computational social science.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eR. Michael Alvarez (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781107107885, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 March 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e312 pages, 46 b\/w illus.  2 maps  18 tables\u003cbr\u003e23.5 x 16 x 2.3 cm, 0.58 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'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\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eQuantitative 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface Gary King\u003cbr\u003e Introduction R. Michael Alvarez\u003cbr\u003e 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\u003cbr\u003e 2. Navigating the local modes of big data: the case of topic models Margaret Roberts, Brandon Stewart and Dustin Tingley\u003cbr\u003e 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\u003cbr\u003e 4. Network structure and social outcomes: network analysis for social science Betsy Sinclair\u003cbr\u003e 5. Ideological salience in multiple dimensions Peter Foley\u003cbr\u003e 6. Random forest applied to feature selection in biomedical research Daniel Conn and Christina Ramirez\u003cbr\u003e 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\u003cbr\u003e 8. Measuring representational style in the House: the Tea Party, Obama and legislators' changing expressed priorities Justin Grimmer\u003cbr\u003e 9. Using social marketing and data science to make government smarter Brian Griepentrog, Sean Marsh, Sidney Carl Turner and Sarah Evans\u003cbr\u003e 10. Using machine algorithms to detect election fraud Ines Levin, Julia Pomares and R. Michael Alvarez\u003cbr\u003e 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\u003cbr\u003e Conclusion. Computational social science: towards a collaborative future Hanna Wallach.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Research methods: general [\u003ca title=\"See our other books on Research methods: general\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Research%20methods:%20general%20%5BGPS%5D%22\"\u003eGPS\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46005546385688,"sku":"9781107107885","price":90.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781107107885i_f20d1955-1279-46e7-a383-ee5109263846.jpg?v=1696793276","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computational-social-science-discovery-and-prediction-hardback-9781107107885","provider":"Freshly Printed Books","version":"1.0","type":"link"}