E-Book Overview
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.
E-Book Content
Computational Social Sciences
Shu-Heng Chen Editor
Big Data in Computational Social Science and Humanities
Computational Social Sciences
Computational Social Sciences A series of authored and edited monographs that utilize quantitative and computational methods to model, analyze and interpret large-scale social phenomena. Titles within the series contain methods and practices that test and develop theories of complex social processes through bottom-up modeling of social interactions. Of particular interest is the study of the co-evolution of modern communication technology and social behavior and norms, in connection with emerging issues such as trust, risk, security and privacy in novel socio-technical environments. Computational Social Sciences is explicitly transdisciplinary: quantitative methods from fields such as dynamical systems, artificial intelligence, network theory, agentbased modeling, and statistical mechanics are invoked and combined with state-of-theart mining and analysis of large data sets to help us understand social agents, their interactions on and offline, and the effect of these interactions at the macro level. Topics include, but are not limited to social networks and media, dynamics of opinions, cultures and conflicts, socio-technical co-evolution and social psychology. Computational Social Sciences will also publish monographs and selected edited contributions from specialized conferences and workshops specifically aimed at communicating new findings to a large transdisciplinary audience. A fundamental goal of the series is to provide a single forum within which commonalities and differences in the workings of this field may be discerned, hence leading to deeper insight and understanding. Series Editors Elisa Bertino Purdue University, West Lafayette, IN, USA
Larry S. Liebovitch Queens College, City University of New York, Flushing, NY, USA
Claudio Cioffi-Revilla George Mason University, Fairfax, VA, USA
Sorin A. Matei Purdue University, West Lafayette, IN, USA
Jacob Foster University of California, Los Angeles, CA, USA
Anton Nijholt University of Twente, Enschede, The Netherlands
Nigel Gilbert University of Surrey, Guildford, UK
Andrzej Nowak University of Warsaw, Warsaw, Poland
Jennifer Golbeck University of Maryland, College Park, MD, USA
Robert Savit University of Michigan, Ann Arbor, MI, USA
Bruno Gonçalves New York Univ