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Cluster-Based Early Warning Indicators for Political Change in the Contemporary Levant

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  • Schrodt, Philip A.
  • Gerner, Deborah J.

Abstract

We use cluster analysis to develop a model of political change in the Levant as reflected in the World Event Interaction Survey coded event data generated from Reuters between 1979 and 1998. A new statistical algorithm that uses the correlation between dyadic behaviors at two times identifies clusters of political activity. The transition to a new cluster occurs when a point is closer in distance to subsequent points than to preceding ones. These clusters begin to “stretch” before breaking apart, which serves as an early warning indicator. The clusters correspond well with phases of political behavior identified a priori. A Monte Carlo analysis shows that the clustering and early warning measures are not random; they perform very differently in simulated data sets with similar statistical characteristics. Our study demonstrates that the statistical analysis of newswire reports can yield systematic early warning indicators, and it provides empirical support for the theoretical concept of distinct behavioral phases in political activity.

Suggested Citation

  • Schrodt, Philip A. & Gerner, Deborah J., 2000. "Cluster-Based Early Warning Indicators for Political Change in the Contemporary Levant," American Political Science Review, Cambridge University Press, vol. 94(4), pages 803-817, December.
    • Handle: RePEc:cup:apsrev:v:94:y:2000:i:04:p:803-817_22
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    Cited by:

    1. Patrick T. Brandt & Michael Colaresi & John R. Freeman, 2008. "The Dynamics of Reciprocity, Accountability, and Credibility," Journal of Conflict Resolution, Peace Science Society (International), vol. 52(3), pages 343-374, June.
    2. Patrick T. Brandt & John R. Freeman & Philip A. Schrodt, 2011. "Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 28(1), pages 41-64, February.
    3. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    4. Matthew J. Lebo & Janet M. Box‐Steffensmeier, 2008. "Dynamic Conditional Correlations in Political Science," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 688-704, July.
    5. Brandt, Patrick T. & Freeman, John R. & Schrodt, Philip A., 2014. "Evaluating forecasts of political conflict dynamics," International Journal of Forecasting, Elsevier, vol. 30(4), pages 944-962.
    6. Vito D'Orazio & James E Yonamine, 2015. "Kickoff to Conflict: A Sequence Analysis of Intra-State Conflict-Preceding Event Structures," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-21, May.
    7. Steven C. Poe & Nicolas Rost & Sabine C. Carey, 2006. "Assessing Risk and Opportunity in Conflict Studies," Journal of Conflict Resolution, Peace Science Society (International), vol. 50(4), pages 484-507, August.
    8. James A. Piazza, 2013. "Regime Age and Terrorism: Are New Democracies Prone to Terrorism?," International Interactions, Taylor & Francis Journals, vol. 39(2), pages 246-263, April.
    9. Faruk Balli & Hatice Ozer Balli & Mudassar Hasan & Russell Gregory-Allen, 2022. "Geopolitical risk spillovers and its determinants," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(2), pages 463-500, April.

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