Classifying Dyads for Militarized Conflict Analysis

TL;DR

This paper compares bi-lateral and multi-lateral systemic causes of militarized conflict by using textual and graph-based features from Wikipedia to classify entity dyads as allies or enemies.

Contribution

It introduces a novel dyad classification approach using Wikipedia-derived features to analyze the correlation of systemic and dyadic causes with conflict.

Findings

Systemic features slightly outperform dyadic features in correlating with conflict.

Wikipedia articles of allies are more semantically similar than those of enemies.

Graph-based modeling effectively captures relational data for conflict analysis.

Abstract

Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is to compare these two causes in terms of how they correlate with conflict between two entities. We do this by devising a set of textual and graph-based features which represent each of the causes. The features are extracted from Wikipedia and modeled as a large graph. Nodes in this graph represent entities connected by labeled edges representing ally or enemy-relationships. This allows casting the problem as an edge classification task, which we term dyad classification. We propose and evaluate classifiers to determine if a particular pair of entities are allies or enemies. Our results suggest that our systemic features might be slightly better correlates of conflict. Further, we find that Wikipedia articles of allies are semantically more similar than enemies.