Big Data in Computational Social Sciences and Humanities: An Introduction

  • Shu-Heng Chen
  • Tina Yu
Part of the Computational Social Sciences book series (CSS)


This chapter provides an overview of the current development of big data in the computational social sciences and humanities. It is composed of two parts. In the first part, we review works incorporating the three most frequently seen types of big data, namely geographic data, text corpus data, and social media data, that are used to conduct research on the social sciences 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 chapter provides a panoramic view of the development of big data in the computational social sciences and humanities, including recent trends and the evoked challenges. As for the former, we review four representative cases of its timely development. They are big data finance, big data in psychology, the spatial humanities, and cloud computing. As for the latter, we present an overview of four challenges associated with big data, namely the complexity of big data or the ontology and epistemology of big data, big data search, big data simulation, and big data risk.


Big data Computational social science Computational humanities Citizen science Geographic information system Corpus linguistics Narrative economics Sentiment analysis Multiplex networks Spatial humanities Computational history 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shu-Heng Chen
    • 1
  • Tina Yu
    • 1
  1. 1.AI-ECON Research Center, Department of EconomicsNational Chengchi UniversityTaipeiTaiwan

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