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Computational Social Science of Disasters: Opportunities and Challenges

1
Department of Computational and Data Sciences, George Mason University, Fairfax, VA 4400, USA
2
Center for Social Complexity, George Mason University, Fairfax, VA 4400, USA
3
Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA 4400, USA
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(5), 103; https://doi.org/10.3390/fi11050103
Received: 20 March 2019 / Revised: 19 April 2019 / Accepted: 23 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue 10th Anniversary Feature Papers)
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Abstract

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments. View Full-Text
Keywords: disasters; computational social science; crisis informatics; disaster modeling; Web 2.0; social media; big data; volunteered geographical information; crowdsourcing disasters; computational social science; crisis informatics; disaster modeling; Web 2.0; social media; big data; volunteered geographical information; crowdsourcing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Burger, A.; Oz, T.; Kennedy, W.G.; Crooks, A.T. Computational Social Science of Disasters: Opportunities and Challenges. Future Internet 2019, 11, 103.

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