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Multi-source models for civil unrest forecasting

  • Gizem Korkmaz
  • Jose Cadena
  • Chris J. Kuhlman
  • Achla Marathe
  • Anil Vullikanti
  • Naren Ramakrishnan
Original Article

Abstract

Civil unrest events (protests, strikes, and “occupy” events) range from small, nonviolent protests that address specific issues to events that turn into large-scale riots. Detecting and forecasting these events is of key interest to social scientists and policy makers because they can lead to significant societal and cultural changes. We forecast civil unrest events in six countries in Latin America on a daily basis, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report, which is compiled by an independent group of social scientists and subject matter experts. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets. The experimental results, measured by F1-scores, are in the range 0.68–0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our method. The ablation study demonstrates the relative value of data sources for prediction. We find that social media and news are more informative than other data sources, including the political event databases, and enhance the prediction performance. However, social media increases the variation in the performance metrics.

Keywords

Social Medium Receiver Operating Characteristic Curve Multiple Data Source News Source Social Media Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been partially supported by the following Grants: DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0010, NSF ICES CCF-1216000, NSF NETSE Grant CNS-1011769 and NIH 1R01GM109718. Also, supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) Contract No. D12PC000337, the US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Biocomplexity Institute of Virginia TechArlingtonUSA
  2. 2.Biocomplexity Institute of Virginia TechBlacksburgUSA
  3. 3.Discovery Analytics Center, Virginia TechArlingtonUSA

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