Big Data in Computational Social Science and Humanities [electronic resource] /
Chen, Shu-Heng.editor.
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.
Includes bibliographical references and index.
Big Data in Computational Social Sciences and the Humanities: An Introduction -- Part I: Practice -- Application of Citizen Science and Volunteered Geographic Information (VGI): Tourism Development for Rural Communities -- Telling Stories Through R: Geo-temporal Mappings of Epigraphic -- Expressing Dynamic Maps through 17th-Century Taiwan Dutch Manuscripts -- Has Homo economicus Evolved into Homo sapiens from 1992 to 2014?: What Does Corpus Linguistics Say? -- Big Data and FinTech -- Health in Biodiversity-Related Conventions: Analysis of a Multiplex Terminological Network (1973-2016) -- How Does Linguistic Complexity in Shakespeare's Plays Relate to the Production History of a Commercial American Theatre? -- Language Communities, Corpora, and Cognition -- From Naive Expectation to Realistic Progress - Government Applications of Big Data to Public Opinions Mining -- Understanding "the User-Generated": The Construction of the "ABC model" and the Imagination of "Digital Humanities" -- Part II: Survey and Challenges -- Big Data Finance and Financial Markets -- Applications of Internet Methods in Psychology -- Spatial Humanities: An Integrated Approach to Spatiotemporal Research -- Cloud Computing in the Social Sciences and Humanities -- Analysis of Social Media Data: An Introduction to the Characteristics and Chronological Process Spatial Humanities -- Big Data and Research Opportunities Using HRAF Databases -- Computational History: From Big Data to Big Simulations -- A Posthumanist Reflection on the Digital Humanities and Social Sciences.
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.
Data mining.
Behavioral economics.
Big data.
Natural language processing (Computer science).
Philosophy.
Political science.
Data Mining and Knowledge Discovery.
Behavioral/Experimental Economics.
Big Data/Analytics.
Natural Language Processing (NLP).
Philosophy of Technology.
Political Science.