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2018 | OriginalPaper | Chapter

1. Big Data in Computational Social Sciences and Humanities: An Introduction

Authors : Shu-Heng Chen, Tina Yu

Published in: Big Data in Computational Social Science and Humanities

Publisher: Springer International Publishing

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Abstract

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.

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next chapter Application of Citizen Science and Volunteered Geographic Information (VGI): Tourism Development for Rural Communities
Footnotes
1
Computational social sciences, as the title of this book series demonstrates, require little explanation. The term, computational humanities, however, is less popular. Gerhard Heyer distinguishes digital humanities from computational humanities as follows. The former is the creation, dissemination, and use of digital repositories, and the latter is the computer-based analysis of digital repositories using advanced computational and algorithmic methods (Biemann et al. 2014). Alternatively, “[c]omputational humanities is an emerging field that bridges the sciences and humanities with the goal of creating accurate computer simulations of historical, social, cultural, and religious events (Cruz-Neira 2003, p. 10).” See Gavin (2014) for a demonstration of the above two descriptions of computational humanities.
 
2
For the related applications of GIS to the humanities, also see Chaps. 3, 4, and 14. In fact, these four chapters can together be read as part of the spatial humanities.
 
3
For a general understanding of citizen science, also known as crowd science, and its recent development, the interested reader is referred to Cooper (2017) and Franzoni and Sauermann (2014).
 
4
Richard Thaler is the 2017 Nobel Laureate in Economics.
 
5
There is a philosophical issue as to whether machines will evolve to have their own interpretations of the text and hence develop their own emotions which are different from those of general human beings under the governance of their own culture. More positively, would machines surpass humans by demonstrating the features of positive psychology, as advocated by Martin Seligman (Seligman 2004), more successfully than humans?
 
6
There are already quite a few good references giving a panoramic guide to this fast growing field. The interested reader is referred to Liu (2015), Pozzi et al. (2016), and Cambria et al. (2017).
 
7
While there are only two chapters collected in this volume, the interested reader may find more useful references in Peterson (2016) and the excellent collections edited by Mitra and Xiang (2016). However, sentiment analysis may go further, beyond what the current literature delineates, and can be further incorporated into agent-based computational finance and give new impetus to behavioral finance (Chen and Venkatachalam 2017).
 
8
For example, for the complexity measure for sentiments, see Joshi et al. (2014); for the complexity measure for networks, see Morzy et al. (2017).
 
9
Interested readers are referred to Bauerlein (2008), Sunstein (2008), Ceron et al. (2016), Thompson (2016), Helbing et al. (2017), O’Neil (2017), and Stephens-Davidowitz and Pabon (2017).
 
10
The PTT Bulletin Board System is the largest terminal-based bulletin board system (BBS) based in Taiwan. For more information, see https://​en.​wikipedia.​org/​wiki/​PTT_​Bulletin_​Board_​System.
 
11
The conundrum has been well illustrated by the so-called adaptive market hypothesis, which endowed the efficient markets hypothesis with a dynamic and evolutionary interpretation (Lo 2004). In the vein of the agent-based fashion, the adaptive market hypothesis has been further studied in the form of the market fraction hypothesis (Chen et al. 2010).
 
12
This project is carried out within a collaboration between the Kavli Foundation, the Institute for the Interdisciplinary Study of Decision Making at New York University (NYU), and the NYU Center for Urban Science and Progress. For more details, the interested reader is referred to Azmak et al. (2015).
 
13
The current use of big data in psychology is not just exhausted by the survey presented in this chapter. The journal Psychological Methods has published a special issue on this frontier (Harlow and Oswald 2016). For other developments, the interested reader is also referred to Cheung and Jak (2016) and Jones (2016).
 
14
The representativeness heuristic is one of the heuristics that has been carefully studied by psychologists and behavioral economists, regarding how human decisions or judgments are made under uncertainty (Kahneman and Tversky 1972).
 
15
The interested reader is welcome to visit its home page: http://​apsti.​nccu.​edu.​tw/​.
 
16
For a general background of this fast-growing field, the interested reader is referred to Bodenhamer et al. (2010).
 
17
This feature can be coined as the big data paradox, namely too big to be “small.”
 
18
In the development of the computational social sciences and humanities, the role of cyborgs is often ignored. For example, in social simulation or agent-based simulation, there is a clear distinction between human agents and software agents, but their possible hybridizations are left out. See Chen et al. (2018).
 
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Metadata
Title
Big Data in Computational Social Sciences and Humanities: An Introduction
Authors
Shu-Heng Chen
Tina Yu
Copyright Year
2018
Book
Big Data in Computational Social Science and Humanities

Print ISBN: 978-3-319-95464-6

Electronic ISBN: 978-3-319-95465-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-95465-3_1

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