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Big Data in Computational Social Sciences and Humanities: An Introduction

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Big Data in Computational Social Science and Humanities

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

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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Notes

  1. 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. 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. 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. 4.

    Richard Thaler is the 2017 Nobel Laureate in Economics.

  5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15.

    The interested reader is welcome to visit its home page: http://apsti.nccu.edu.tw/.

  16. 16.

    For a general background of this fast-growing field, the interested reader is referred to Bodenhamer et al. (2010).

  17. 17.

    This feature can be coined as the big data paradox, namely too big to be “small.”

  18. 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).

References

  • Azmak, O., Bayer, H., Caplin, A., Chun, M., Glimcher, P., Koonin, S., & Patrinos, A. (2015). Using Big data to understand the human condition: The Kavli HUMAN project. Big Data, 3(3), 173–188.

    Article  Google Scholar 

  • Bauerlein, M. (2008). The dumbest generation: How the digital age stupefies young Americans and jeopardizes our future (or, don’t trust anyone under 30). London: Penguin.

    Google Scholar 

  • Biemann, C., Crane, G. R., Fellbaum, C. D., & Mehler, A. (2014). Computational humanities-bridging the gap between computer science and digital humanities (Dagstuhl Seminar 14301). In Dagstuhl reports (Vol. 4, No. 7). Dagstuhl: Schloss Dagstuhl-Leibniz-Zentrum für Informatik.

    Google Scholar 

  • Bodenhamer, D. J., Corrigan, J., & Harris, T. M. (Eds.). (2010). The spatial humanities: GIS and the future of humanities scholarship. Bloomington: Indiana University Press.

    Google Scholar 

  • Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (Eds.). (2017). A practical guide to sentiment analysis (Vol. 5). Heidelberg: Springer.

    Google Scholar 

  • Ceron, A., Curini, L., & Iacus, S. M. (2016). Politics and Big data: Nowcasting and forecasting elections with social media. Didcot: Taylor & Francis.

    Book  Google Scholar 

  • Chen, S.-H. (2008). Financial applications: Stock markets. In B. Wang (Ed.), Wiley encyclopedia of computer science and engineering (pp. 1227–1244). Hoboken: Wiley.

    Google Scholar 

  • Chen, S.-H. (2013). Reasoning-based artificial agents in agent-based computational economics. In K. Nakamatsu & L. Jain (Eds.), The handbook on reasoning-based intelligent systems (pp. 575–602). Singapore: World Scientific.

    Chapter  Google Scholar 

  • Chen, S.-H., & Venkatachalam, R. (2017). Agent-based modelling as a foundation for big data. Journal of Economic Methodology, 24(4), 362–383.

    Article  Google Scholar 

  • Chen, S. H., Kaboudan, M., & Du, Y. R. (2018). Computational economics in the era of natural computationalism. In S. H. Chen, M. Kaboudan, & Y. R. Du (Eds.), The Oxford handbook of computational economics and finance. New York: Oxford.

    Chapter  Google Scholar 

  • Chen, S.-H., Kampouridis, M., & Tsang, E. (2010). Microstructure dynamics and agent-based financial markets. In International workshop on multi-agent systems and agent-based simulation (pp. 121–135). Berlin: Springer.

    Google Scholar 

  • Cheung, M. W. L., & Jak, S. (2016). Analyzing big data in psychology: A split/analyze/meta-analyze approach. Frontiers in Psychology, 7, 738 https://doi.org/10.3389/fpsyg.2016.00738.

    Article  Google Scholar 

  • Clark, A. E., Flèche, S., Layard, R., Powdthavee, N., & Ward, G. (2018). The origins of happiness: The science of Well-being over the life course. Princeton: Princeton University Press.

    Google Scholar 

  • Conover, M. D., Ferrara, E., Menczer, F., & Flammini, A. (2013). The digital evolution of occupy Wall Street. PLoS One, 8(5), e64679.

    Article  Google Scholar 

  • Conroy, N. J., Rubin, V. L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.

    Article  Google Scholar 

  • Cooper, C. (2017). Citizen science: How ordinary people are changing the face of discovery. London: Gerald Duckworth & Co.

    Google Scholar 

  • Cruz-Neira, C. (2003). Computational humanities: The new challenge for VR. IEEE Computer Graphics and Applications, 23(3), 10–13.

    Article  Google Scholar 

  • Franzoni, C., & Sauermann, H. (2014). Crowd science: The organization of scientific research in open collaborative projects. Research Policy, 43(1), 1–20.

    Article  Google Scholar 

  • Gavin, M. (2014). Agent-based modeling and historical simulation. DHQ: Digital Humanities Quarterly, 8(4). Retrieved January 12, 2015, from http://www.digitalhumanities.org/dhq/vol/8/4/000195/000195.html

  • Hansen, S., McMahon, M., & Prat, A. (2018). Transparency and deliberation within the FOMC: A computational linguistics approach. The Quarterly Journal of Economics, 1, 70. https://doi.org/10.1093/qje/qjx045.

    Article  Google Scholar 

  • Harlow, L. L., & Oswald, F. L. (2016). Big data in psychology: Introduction to the special issue. Psychological Methods, 21(4), 447.

    Article  Google Scholar 

  • Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., et al. (2017). Will democracy survive big data and artificial intelligence? Scientific American, 25. Retrieved February 27, 2017, from https://www.scientificamerican.com/article/will-democracy-survive-big-data-and-artificial-intelligence/ (accessed 27 Feb, 2017)

  • Jones, M. N. (Ed.). (2016). Big data in cognitive science. Hove: Psychology Press.

    Google Scholar 

  • Joshi, A., Mishra, A., Senthamilselvan, N., & Bhattacharyya, P. (2014). Measuring sentiment annotation complexity of text. In Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (volume 2: Short papers) (Vol. 2, pp. 36–41).

    Chapter  Google Scholar 

  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.

    Article  Google Scholar 

  • Kleiner, B., Stam, A., & Pekari, A. (2015). Big data for the social sciences (FORS Working Papers, 2015-2).

    Google Scholar 

  • Lane, J., Stodden, V., Bender, S., & Nissenbaum, H. (Eds.). (2014). Privacy, big data, and the public good: Frameworks for engagement. Cambridge: Cambridge University Press.

    Google Scholar 

  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Loader, B. D., Vromen, A., Xenos, M. A., Steel, H., & Burgum, S. (2015). Campus politics, student societies and social media. The Sociological Review, 63(4), 820–839.

    Article  Google Scholar 

  • Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30, 15–29.

    Article  Google Scholar 

  • McCloskey, D. N. (1983). The rhetoric of economics. Journal of Economic Literature, 21(2), 481–517.

    Google Scholar 

  • McCloskey, D. N. (1998). The rhetoric of economics. Madison: University of Wisconsin Press.

    Google Scholar 

  • Mitra, G., & Xiang, Y. (2016). Handbook of sentiment analysis in finance. New York: Albury Books.

    Google Scholar 

  • Morson, G. S., & Schapiro, M. (2017). Cents and sensibility: What economics can learn from the humanities. Princeton: Princeton University Press.

    Book  Google Scholar 

  • Morzy, M., Kajdanowicz, T., & Kazienko, P. (2017). On measuring the complexity of networks: Kolmogorov complexity versus entropy. Complexity, 2017, 3250301.

    Article  MathSciNet  Google Scholar 

  • O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Broadway Books.

    MATH  Google Scholar 

  • Peters, B. (2012). The big data gold rush. New York: Forbes Magazine.

    Google Scholar 

  • Peterson, R. L. (2016). Trading on sentiment: The power of minds over markets. Hoboken: Wiley.

    Book  Google Scholar 

  • Pinheiro, F. L., Santos, M. D., Santos, F. C., & Pacheco, J. M. (2014). Origin of peer influence in social networks. Physical Review Letters, 112(9), 098702.

    Article  Google Scholar 

  • Pozzi, F. A., Fersini, E., Messina, E., & Liu, B. (2016). Sentiment analysis in social networks. Burlington: Morgan Kaufmann.

    Google Scholar 

  • Rossbach, S. (1983). Feng Shui, the Chinese art of placement. New York: EP Dutton. Inc.

    Google Scholar 

  • Roy, D., & Zeckhauser, R. (2016). Literary light on decision’s dark corner. In R. Frantz, S. H. Chen, K. Dopfer, F. Heukelom, & S. Mousavi (Eds.), Routledge handbook of behavioral economics (pp. 230–249). Abingdon: Routledge.

    Google Scholar 

  • Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899.

    Article  Google Scholar 

  • Seligman, M. E. (2004). Authentic happiness: Using the new positive psychology to realize your potential for lasting fulfillment. New York: Simon and Schuster.

    Google Scholar 

  • Shiller, R. J. (2017). Narrative economics. American Economic Review, 107(4), 967–1004.

    Article  Google Scholar 

  • Soja, E. (2001). In different spaces: Interpreting the spatial organization of societies. In Proceedings, 3rd international space syntax symposium (p. 1-s1).

    Google Scholar 

  • Soros, G. (2013). Fallibility, reflexivity, and the human uncertainty principle. Journal of Economic Methodology, 20(4), 309–329.

    Article  Google Scholar 

  • Stephens-Davidowitz, S., & Pabon, A. (2017). Everybody lies: Big data, new data, and what the internet can tell us about who we really are. New York: HarperLuxe.

    Google Scholar 

  • Sunstein, C. R. (2008). Neither Hayek nor Habermas. Public Choice, 134(1–2), 87–95.

    Google Scholar 

  • Thompson, A. (2016). Journalists and Trump voters live in separate online bubbles, MIT analysis shows. New York: Vice News.

    Google Scholar 

  • Vaillant, G. E. (2008). Aging well: Surprising guideposts to a happier life from the landmark study of adult development. Boston: Little, Brown.

    Google Scholar 

  • Webster, R. (2012). Feng Shui for beginners: Successful living by design. Woodbury: Llewellyn Worldwide.

    Google Scholar 

  • WHO-CBD. (2015). Connecting global priorities: biodiversity and human health: a state of knowledge review, p. 344.

    Google Scholar 

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Authors and Affiliations

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

    Shu-Heng Chen & Tina Yu

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

    Shu-Heng Chen

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Chen, SH., Yu, T. (2018). Big Data in Computational Social Sciences and Humanities: An Introduction. In: Chen, SH. (eds) Big Data in Computational Social Science and Humanities. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-95465-3_1

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

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