Toward a Bayesian network model of events in international relations

Abstract

Formal models of international relations have a long history of exploiting representations and algorithms from artificial intelligence. As more news sources move online, there is an increasing wealth of data that can inform the creation of such models. The Global Database of Events, Language, and Tone (GDELT) extracts events from news articles from around the world, where the events represent actions taken by geopolitical actors, reflecting the actors’ relationships. We can apply existing machine-learning algorithms to automatically construct a Bayesian network that represents the distribution over the actions between actors. Such a network model allows us to analyze the interdependencies among events and generate the relative likelihoods of different events. By examining the accuracy of the learned network over different years and different actor pairs, we are able to identify aspects of international relations from a data-driven approach. We are also able to identify weaknesses in the model that suggest needs for additional domain knowledge.