Big Data in Computational Social Science and Humanities

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.

Shu-Heng Chen is a Taiwanese economist and currently a Distinguished Professor at the Department of Economics at National Chengchi University. He is also the founder and Director of the AI-ECON Research Center at the National Chengchi University. His contributions are in the area of computational approaches to understanding economic and finance problems, in particular, the use of heterogeneous agent-based approach and genetic programming in economics. He is considered one of the pioneers in the field of agent-based computational economics and the first to introduce genetic programming to ACE. He takes a biologically-inspired approach in modeling the boundedly rational behavior of agents and is influenced by the work of Herbert A. Simon.