From Newswire to Nexus: Using text-based actor embeddings and transformer networks to forecast conflict dynamics

TL;DR

This paper introduces a novel method combining text-based actor embeddings with transformer models to improve the prediction of conflict escalation and de-escalation at the actor level, leveraging newswire and structured conflict data.

Contribution

It presents a hybrid NLP approach that integrates newswire texts with conflict event data using transformer networks for more accurate conflict forecasting.

Findings

Outperforms traditional models in predicting conflict phases

Effectively captures volatile conflict patterns

Provides granular, actor-level conflict predictions

Abstract

This study advances the field of conflict forecasting by using text-based actor embeddings with transformer models to predict dynamic changes in violent conflict patterns at the actor level. More specifically, we combine newswire texts with structured conflict event data and leverage recent advances in Natural Language Processing (NLP) techniques to forecast escalations and de-escalations among conflicting actors, such as governments, militias, separatist movements, and terrorists. This new approach accurately and promptly captures the inherently volatile patterns of violent conflicts, which existing methods have not been able to achieve. To create this framework, we began by curating and annotating a vast international newswire corpus, leveraging hand-labeled event data from the Uppsala Conflict Data Program. By using this hybrid dataset, our models can incorporate the textual context of news sources along with the precision and detail of structured event data. This combination enables us to make both dynamic and granular predictions about conflict developments. We validate our approach through rigorous back-testing against historical events, demonstrating superior out-of-sample predictive power. We find that our approach is quite effective in identifying and predicting phases of conflict escalation and de-escalation, surpassing the capabilities of traditional models. By focusing on actor interactions, our explicit goal is to provide actionable insights to policymakers, humanitarian organizations, and peacekeeping operations in order to enable targeted and effective intervention strategies.