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ThinkMind // International Journal On Advances in Internet Technology, volume 10, numbers 1 and 2, 2017 // View article inttech_v10_n12_2017_6


Toward Next Generation Social Analytics: A Platform for Analysis of Quantitative, Qualitative, Geospatial, and Temporal Factors of Community Resilience

Authors:
Dennis J. Folds
Clayton J. Hutto
Thomas A. McDermott

Keywords: Computational social science; human social analytics; human-centered data science; sociotechnical systems

Abstract:
Social science stands on the brink of a revolution – or of failure. It needs powerful new tools, methods, and paradigms in order to succeed. These will include advances in computational capabilities, machine-based knowledge assimilation, quantitative analysis, and measurement. Human social analytics in the next generation will need to embrace more multifaceted representations of human behavior with more complex models. Such models will need to integrate data of disparate forms, using disparate units of measure, collected from disparate sources, at disparate scales. The development of a complex model of societal well-being (an inherently qualitative construct) forms the basis of research for a next-generation societal resilience model. The model combines traditionally separate socio-environmental and psychological constructs of resilience, a representation that requires large scale quantitative, geospatial, and temporally referenced data of disparate forms, units, sources, and scales. The research forms a framework for the development of data analytic experimentation platforms in the social sciences. The platform will be used to demonstrate tools and methods that facilitate the progression towards next generational social analytics at large scales. These concepts, tools, and methods are intended to empower social science in transformative ways.

Pages: 70 to 86

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: June 30, 2017

Published in: journal

ISSN: 1942-2652

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