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.

翻译：本研究通过将基于文本的参与者嵌入与Transformer模型相结合，在参与者层面预测暴力冲突模式的动态变化，从而推进了冲突预测领域的发展。具体而言，我们整合了新闻通讯文本与结构化冲突事件数据，并利用自然语言处理（NLP）技术的最新进展，预测政府、民兵、分离主义运动和恐怖组织等冲突参与者之间的冲突升级与降级动态。这一新方法能够准确、及时地捕捉暴力冲突固有的易变模式，而现有方法尚未实现这一目标。为构建此框架，我们首先整理并标注了一个大规模国际新闻通讯语料库，同时利用了乌普萨拉冲突数据计划中人工标注的事件数据。通过使用这种混合数据集，我们的模型能够融合新闻源的文本语境以及结构化事件数据的精确细节。这种结合使我们能够对冲突发展做出动态且细粒度的预测。我们通过对历史事件进行严格回测验证了本方法的有效性，并展示了其卓越的样本外预测能力。研究发现，本方法在识别和预测冲突升级与降级阶段方面非常有效，其性能超越了传统模型。通过聚焦于参与者间的互动，我们的明确目标是为政策制定者、人道主义组织和维和行动提供可操作的见解，以支持精准有效的干预策略。