DocumentCode :
2882625
Title :
Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data
Author :
Manrique, Pedro ; Hong Qi ; Morgenstern, Ana ; Velasquez, Nicolas ; Tsai-Ching Lu ; Johnson, Nadiyah
Author_Institution :
Phys. Dept., Univ. of Miami, Miami, FL, USA
fYear :
2013
fDate :
4-7 June 2013
Firstpage :
169
Lastpage :
172
Abstract :
Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.
Keywords :
data handling; public domain software; social sciences computing; GT; Google Trends; OSI; big data; civil unrest forecasting; context matters; extreme social behavior; open source indicators; social dynamics; Context; Data handling; Data storage systems; Educational institutions; Information management; Laboratories; Predictive models; big data; civil unrest; emerging phenomena; open source indicator; prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4673-6214-6
Type :
conf
DOI :
10.1109/ISI.2013.6578812
Filename :
6578812
Link To Document :
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