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Multi-source models for civil unrest forecasting

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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.

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Notes

  1. June 21, 2013, “Protesters, criminals get around government censors using secret web network,” http://bit.ly/1Sghvo7.

  2. This model was briefly mentioned, along with several others in Ramakrishnan et al. (2014) as part of an automated, real-time forecasting software system. This paper describes our model and results in detail.

  3. http://lanic.utexas.edu/.

  4. The dictionary is compiled by a different group of experts from the one that generated the GSR.

  5. Source: Bloomberg, http://www.bloomberg.com/news/articles/2014-02-14/twitter-says-venezuela-blocks-its-images-amid-protest-crackdown.

  6. Source: Yahoo news, http://news.yahoo.com/turkey-arrests-3-raids-over-erdogan-twitter-insults-155612123.html.

  7. Source: https://www.torproject.org/.

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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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Authors and Affiliations

  1. Biocomplexity Institute of Virginia Tech, 900 N Glebe Rd, Arlington, VA, 22203, USA

    Gizem Korkmaz

  2. Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA, 24061-0477, USA

    Jose Cadena, Chris J. Kuhlman, Achla Marathe & Anil Vullikanti

  3. Discovery Analytics Center, Virginia Tech, 900 N Glebe Rd, Arlington, VA, 22203, USA

    Naren Ramakrishnan

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  1. Gizem Korkmaz
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  2. Jose Cadena
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  4. Achla Marathe
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  5. Anil Vullikanti
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  6. Naren Ramakrishnan
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Correspondence to Gizem Korkmaz.

Additional information

A preliminary version of the paper appeared in the Proceedings of 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Korkmaz et al. 2015).

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Korkmaz, G., Cadena, J., Kuhlman, C.J. et al. Multi-source models for civil unrest forecasting. Soc. Netw. Anal. Min. 6, 50 (2016). https://doi.org/10.1007/s13278-016-0355-8

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