Doing Computational Social Science: A Practical Introduction. This item is unavailable.
Language: English
Published by SAGE Publications Ltd, 2021
- First Edition
- Softcover
- New

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2022. 1st Edition. Paperback. . . . . .
Seller Inventory # V9781526468185
- Title
- Doing Computational Social Science: A Practical Introduction
- Author
- John McLevey
- Publisher
- SAGE Publications Ltd
- Publication year
- 2021
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1526468182
- ISBN 13
- 9781526468185
- Edition
- 1st Edition
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"Synopsis" may belong to another edition of this title.
About the Author
John McLevey is an Associate Professor in the Department of Knowledge Integration at the University of Waterloo (ON, Canada). He is also appointed to the Departments of Sociology & Legal Studies and Geography and Environmental Management, is a Policy Fellow at the Balsillie School of International Affairs, and a Member of the Cybersecurity and Privacy Institute at the University of Waterloo. His work is funded by research grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation.
His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.
His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.
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