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Big Data in Computational Social Science and Humanities

  • Book
  • © 2018

Overview

Editors:
  1. Shu-Heng Chen

    1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

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  • Provides a comprehensive overview of the use of big data in various disciplines of the social sciences and humanities
  • Includes self-contained and easy-accessible chapters for big data governance
  • Exemplifies the new kind of interdisciplinary collaborations in the big-data era
  • Shows how the digital society brings in methodological innovations to social sciences and humanities

Part of the book series: Computational Social Sciences (CSS)

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Big Data in Computational Social Sciences and Humanities: An Introduction

    • Shu-Heng Chen, Tina Yu
    Pages 1-25

  3. Practice

    1. Front Matter

      Pages 27-27
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    2. Application of Citizen Science and Volunteered Geographic Information (VGI): Tourism Development for Rural Communities

      • Jihn-Fa Jan
      Pages 29-44

    3. Telling Stories Through R: Geo-Temporal Mappings of Epigraphic Practices on Penghu

      • Oliver Streiter
      Pages 45-93

    4. Expressing Dynamic Maps Through Seventeenth-Century Taiwan Dutch Manuscripts

      • Ann Heylen
      Pages 95-116

    5. Has Homo economicus Evolved into Homo sapiens from 1992 to 2014: What Does Corpus Linguistics Say?

      • Yawen Zou, Shu-Heng Chen
      Pages 117-137

    6. Big Data and FinTech

      • Jia-Lang Seng, Yao-Min Chiang, Pang-Ru Chang, Feng-Shang Wu, Yung-Shen Yen, Tzu-Chieh Tsai
      Pages 139-163

    7. Health in Biodiversity-Related Conventions: Analysis of a Multiplex Terminological Network (1973 –2016)

      • Claire Lajaunie, Pierre Mazzega, Romain Boulet
      Pages 165-182

    8. How Does Linguistic Complexity in Shakespeare’s Plays Relate to the Production History of a Commercial American Theater?

      • Brian Kokensparger
      Pages 183-193

    9. Language Communities, Corpora, and Cognition

      • Huei-Ling Lai, Kawai Chui, Wen-Hui Sah, Siaw-Fong Chung, Chao-Lin Liu
      Pages 195-206

    10. From Naive Expectation to Realistic Progress: Government Applications of Big Data on Public Opinions Mining

      • Naiyi Hsiao, Zhoupeng Liao, Don-Yun Chen
      Pages 207-220

    11. Understanding “The User-Generated”: The Construction of the “ABC Model” and the Imagination of “Digital Humanities”

      • Hui-Wen Liu, I-Ying Lin, Ming-Te Chi, Kuo-Wei Hsu
      Pages 221-232

  4. Survey and Challenges

    1. Front Matter

      Pages 233-233
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    2. Big Data Finance and Financial Markets

      • Dehua Shen, Shu-Heng Chen
      Pages 235-248

    3. Applications of Internet Methods in Psychology

      • Lee-Xieng Yang
      Pages 249-262

    4. Spatial Humanities: An Integrated Approach to Spatiotemporal Research

      • David Blundell, Ching-Chih Lin, James X. Morris
      Pages 263-288

    5. Cloud Computing in Social Sciences and Humanities

      • Michael J. Gallagher
      Pages 289-296

    6. Analysis of Social Media Data: An Introduction to the Characteristics and Chronological Process

      • Pai-Lin Chen, Yu-Chung Cheng, Kung Chen
      Pages 297-321

    7. Big Data and Research Opportunities Using HRAF Databases

      • Michael D. Fischer, Carol R. Ember
      Pages 323-336
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Keywords

About this book

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.


Editors and Affiliations

  • AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

About the editor

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.

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