Classifying Dyads for Militarized Conflict Analysis (2021)

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.

## introduction researchers have long sought to understand the underlying causes of militarized conflict. the origins of conflict can be broadly categorized as either dyadic or systemic. dyadic pertains to entityspecific idiosyncrasies, competing ideologies @xcite , e.g., dissimilar political systems @xcite , and power differentials, e.g., economic and demographic capabilities @xcite . the mali war is an example of a conflict to which a dyadic cause has been attributed: it was spawned from differing cultural and ideological identities between the azawad liberation movement and the malian government @xcite . throughout this 1 our dataset can be explored in an interactive dashboard: https://conflict-ai.github.io/conflictwiki . data, code and documentations are provided at https://github.com/conflictai/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 allies or enemies. we term this task dyad classification. a systemic cause, on the other hand, is a cause based on the broader relationship network involving a larger set of entities @xcite . for instance, the intervention of france in the mali war may be said to have had a systemic cause as its origins may be partly attributed to nato's close diplomatic ties with the economic community of @xcite . determining a systemic cause may be aided by the analysis of a graph that encodes the relationships between the entities in the conflict. another example of a systemic origin of conflict is the ancient proverb "the enemy of my enemy is my friend", which is also known as the structural balance theory @xcite . in this paper, we construct textual and graphbased features that encode dyadic and systemic correlates of conflict. we take this approach since establishing causality from our data is a highly complex endeavor, so we focus on correlates instead. our approach uses wikipedia data to compare the ability of classifiers trained using these features to predict whether two entities are allies or enemies. this is illustrated at a high level in fig. 1 . we then perform an ablation study by systematically leaving out dyadic and systemic features to ascertain to what degree these features correlate with whether two entities are enemies or allies in a conflict. our systemic model obtains an f1 score of 0.917 and our dyadic model obtains an f1 score of 0.873. if one believes our features to be representative dyadic and systemic correlates, then this result provides support for the claim that, in aggregate, systemic causes may play a slightly larger role. moreover, we also find that articles of allies are semantically more similar than enemies. ## dyadic and systemic features the larger scientific mission of this paper is to investigate whether, when analyzing a large corpus of conflicts, dyadic or systemic correlates of conflict are more prominent. to carry out such a study, we construct features that encode the notions of dyadic and systemic and train classifiers to predict whether two entities are enemies or allies using these features. the classifiers are designed to operate on a dyad graph, an undirected graph where each node corresponds to an entity and each edge corresponds to the relationship between two entities. this allows casting the problem as an edge classification task, which we term dyad classification. in this section we describe the features accessible by both models, deferring the construction of the dyad graph from wikipedia to §3 and the actual technical implementation of the models to §4. notation. let @xmath0, e) be the dyad graph consisting of entity nodes n and labeled relationship edges e. this graph can be equivalently represented by the set of all dyads @xmath1. each dyad d @xmath2, v i , e i , y i ) consists of two entities u i , v @xmath3, v i ) ∈ e, which is labeled as y @xmath4, enemies}. ## constructing the dyad graph we now turn to the problem of extracting a dyad graph from wikipedia. we first retrieve conflict, e.g., the mali war, and entity, e.g., france, al-qaeda, articles from wikipedia foot_0 ( §3.1) and preprocess the articles to obtain vector representations of articles and their sections ( §4.1). the resulting conflictwiki dataset is a collection of articles on militarized conflict and their involved entities. data, code and documentation are publicly available in an interactive dashboard. a subset of the dyad graph is shown in fig. 4 . ## experimental setup what remains to be discussed is the conversion of articles to a machine-readable representation ( §4.1), the technical implementation of each of these models, and the baselines and setup of the experiments ( §4.2). ## results the results of our main comparison are shown in the top half of tab. 2. we evaluate results in terms of the f1 score, which is the weighted average of the precision and recall. we observe a higher binary f1 score with the systemic model s (@xmath5.917) than with the dyadic model d (@xmath6.873). this difference is significant at @xmath7.05 under a permutation test. we also find that the combined model c achieves @xmath8.926, slightly outperforming the models that use only dyadic or systemic features. this asserts that, if our features are to be taken as good representatives of dyadic and systemic correlates, then our results would suggest that conflicts may be better explained by systemic causes rather than dyadic ones. we conduct two additional analyses to gain further insight into our results. the first is an ablation study of features, to shed light onto the strongest dyadic and systemic correlates ( §5.1). the second is a comparative analysis of the article sections that are most similar between allies and enemies ( §5.2). ## conclusion this work explores the extent to which dyadic and systemic features correlate with whether two entities are allies or enemies. our results suggest that both features are correlated, although, if one is to believe our featurizations and models, systemic features appear to be more correlated. we conduct an ablation study to identify the overall contribution of individual dyadic and systemic features, and a textual similarity study which shows that articles of allies exhibit more similarity than those of enemies.