Discovering thematic structure in political datasets

Abstract

Big data for security informatics requires an analysis of both actors and measurement instruments. By analogy with social and political network analyses, primary questions involve who is doing what and in concert with whom. We seek to examine these questions using cluster analysis, non-linear dimensionality reduction, and machine learning techniques. We apply them to available political science and international relations databases, as these are natural proxies for security informatics databases. In particular we develop an embedding/clustering algorithm that reveals those political themes driving UN voting patterns as well as IGO (Inter-Governmental Organization) memberships. Our algorithm could readily be applied to international conflict, urban crime, and military engagement databases.

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