Big Data in Computational Social Science and Humanities

Big Data in Computational Social Science and Humanities

Author: Shu-Heng Chen

Publisher: Springer

ISBN: 3319954644

Category: Computers

Page: 407

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This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.
Big Data in Computational Social Science and Humanities
Language: en
Pages: 407
Authors: Shu-Heng Chen
Categories: Computers
Type: BOOK - Published: 2018-09-26 - Publisher: Springer

This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography,
Big Data and Social Science
Language: en
Pages: 356
Authors: Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane
Categories: Mathematics
Type: BOOK - Published: 2016-09-15 - Publisher: CRC Press

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools
Big Data and Social Science
Language: en
Pages: 391
Authors: Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane
Categories: Mathematics
Type: BOOK - Published: 2020-11-18 - Publisher: CRC Press

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the
Data Science and Social Research
Language: en
Pages: 300
Authors: N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino
Categories: Social Science
Type: BOOK - Published: 2017-11-17 - Publisher: Springer

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications.
Big Data and Social Science
Language: en
Pages: 391
Authors: Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane
Categories: Mathematics
Type: BOOK - Published: 2020-11-17 - Publisher: CRC Press

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the