Replication data for "Using Past Violence and Current News to Predict Changes in Violence" by Mueller and Rauh (2022)

Replication material for the ViEWS prediction competition entry by Hannes Mueller and Christopher Rauh. The accompanying article for the special issue explains the new method for predicting escalations and de-escalations of violence using a model which relies on conflict history and text features. The text features are generated from over 3.5 million newspaper articles using a so-called topic-model. We show that the combined model relies to a large extent on conflict dynamics, but that text is able to contribute meaningfully to the prediction of rare outbreaks of violence in previously peaceful countries. Given the very powerful dynamics of the conflict trap these cases are particularly important for prevention efforts.

本数据集为汉内斯·穆勒（Hannes Mueller）与克里斯托弗·劳（Christopher Rauh）提交的ViEWS预测竞赛参赛作品的复现材料。本期特刊的配套文章阐释了一种全新的暴力事件升级与降级预测方法：该模型以冲突历史数据与文本特征为核心构建依据。其中文本特征通过所谓的主题模型（topic model）从逾350万篇报刊文章中提取生成。研究证实，该融合模型在很大程度上依托冲突动态演化规律，但文本特征能够为预测此前长期处于和平状态的国家中罕见的暴力突发事件提供实质性助力。鉴于冲突陷阱（conflict trap）极强的动态传导特性，这类案例对于暴力预防工作而言尤为关键。