Classifying Dyads for Militarized Conflict Analysis

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7775–7784 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 7775 Classifying Dyads for Militarized Conflict Analysis Niklas StoehrQ Lucas Torroba Hennigen@ Samin Ahbab Robert WestN Ryan CotterellQ,D QETH Zürich @MIT NEPFL DUniversity of Cambridge niklas.stoehr@inf.ethz.ch lucastor@mit.edu saminahbab0@gmail.com robert.west@epfl.ch ryan.cotterell@inf.ethz.ch Abstract Understanding the origins of militarized con- flict is a complex, yet important undertaking. Existing research seeks to build this under- standing by considering bi-lateral relation- ships between entity pairs (dyadic causes) and multi-lateral relationships among multiple en- tities (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 con- nected 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 re- sults suggest that our systemic features might be slightly better correlates of conflict. Fur- ther, we find that Wikipedia articles of allies are semantically more similar than enemies.1 1 Introduction Researchers have long sought to understand the un- derlying causes of militarized conflict. The origins of conflict can be broadly categorized as either dyadic or systemic. specific idiosyncrasies,Dyadic pertains to entity- competing ideologies (Leader Maynard, 2019), e.g., dissimilar political systems (Rousseau et al., 1996), and power differentials, e.g., economic and demographic capabilities (Geller, 1993). The Mali War is an example of a conflict to which a dyadic cause has been attributed: It was spawned from differing cul- tural and ideological identities between the Azawad Liberation Movement and the Malian government (Chauzal and Damme, 2015). Throughout this 1Our dataset can be explored in an interactive dashboard: https://conflict-ai.github.io/conflictwiki. Data, code and documentations are provided at https://github.com/conflict- ai/conflictwiki. Al-Qaeda Religion Leadership belligerents Mali War enemies Mali Military Religion France Religion Politics Economy v Azawad Liberation Movement History Culture edge to be classified as allies or enemies dyadic features systemic features dyad graph Figure 1: Overview of our approach. From Wikipedia articles on conflicts, we extract the belligerents table in the infobox. This allows constructing a dyad graph in which nodes represent entities connected by labeled edges representing ALLY or ENEMY-relationships. We then compare dyadic and systemic features in terms of how effective they are at classifying two entities as al- lies or enemies. We term this task dyad classification. paper, we use the term dyad to not only denote con- flictual entity pairs (ENEMIES) but also cooperative pairs (ALLIES) in a conflict (Geller, 1993). A systemic cause, on the other hand, is a cause based on the broader relationship network involving a larger set of entities (Sweeney, 2004; Rasler and Thompson, 2010). For instance, the intervention of France in …

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