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

This article uses cluster analysis to develop an early warning model of political change in the Levant as reflected in WEIS-coded event data generated from Reuters between April 1979 and December 1998. We employ a new statistical algorithm that uses the correlation between dyadic behaviors at two time points in time to identify clusters of political activity. The transition to a new cluster occurs when a point is closer in distance to subsequent points than to preceding points. These data clusters begin to "stretch" prior to breaking apart; this characteristic can be used as an early-warning indicator. The clusters identified by this technique 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. These results demonstrate that the statistical analysis of newswire reports can be used to provide systematic early warning indicators and provides empirical support for the theoretical concept of distinct behavioral "phases" in political activity.