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Arab Spring: from newspaper

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Abstract

Agent-based simulation models are an important methodology for explaining social behavior and forecasting social change. However, a major drawback to using such models is that they are difficult to instantiate for specific cases and so are rarely reused. We describe a text-mining network analytic approach for rapidly instantiating a model for predicting the tendency toward revolution and violence based on social and cultural characteristics of a large collection of actors. We illustrate our approach using an agent-based dynamic network framework, Construct, and newspaper data for the 16 countries associated with the Arab Spring. We assess the overall accuracy of the base model across independent runs for 20 different months during the Arab Spring, observing that although predictions led to several false positives, the model is able to predict revolution before it occurs in three of the four nations in which the government was successfully overthrown.

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Notes

  1. Code used for this article, as well as a sample of the data, can be found at https://github.com/kennyjoseph/arabspring.

  2. We will hereafter refer to a set of articles as the collection of all articles within a single month.

  3. See (Greenwald and Banaji 1995; Hilton and von Hippel 1996) for reviews of the plethora of social psychology literature addressing stereotyping.

  4. E.g. the FuzzyWuzzy python module, https://github.com/seatgeek/fuzzywuzzy, used by companies like StubHub.

  5. Trivially, see http://brenocon.com/blog/2012/04/f-scores-dice-and-jaccard-set-similarity/ for a derivation.

  6. http://www.economist.com/node/21550328.

  7. So, for example, the intra-country range for Egypt’s change in violence belief in January of 2011 would be computed as the IQR of Egypt’s change in violence belief from all runs covering January of 2011 or November or December of 2010.

  8. See, e.g., http://www.guardian.co.uk/world/interactive/2011/mar/22/middle-east-protest-interactive-timeline.

  9. All replications for a single month were run in parallel using 8 cores of a 60 core machine with a 250 GB SSD drive and 120 GB of RAM.

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Acknowledgments

This work was supported in part by the Air Force Office of Sponsored Research, FA9550-11-1-0179 and the Office of Naval Research through a Minerva N000141310835. Additional support was provided by the center for Computational Analysis of Social and Organizational Systems and the Institute for Software Research at Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, or the U.S. government.

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Authors and Affiliations

  1. Computation, Organizations and Society Program, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA

    Kenneth Joseph, Kathleen M. Carley, David Filonuk, Geoffrey P. Morgan & Jürgen Pfeffer

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  1. Kenneth Joseph
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  2. Kathleen M. Carley
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  3. David Filonuk
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  4. Geoffrey P. Morgan
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  5. Jürgen Pfeffer
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Correspondence to Kenneth Joseph.

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Joseph, K., Carley, K.M., Filonuk, D. et al. Arab Spring: from newspaper. Soc. Netw. Anal. Min. 4, 177 (2014). https://doi.org/10.1007/s13278-014-0177-5

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