Mueller, H., Rauh, C. and Seimon, B.

Introducing a Global Dataset on Conflict Forecasts and News Topics

Data & Policy

Vol. 6 (2024)

Abstract: This article provides a structured description of openly available news topics and forecasts for armed conflict at the national and grid cell level starting January 2010. The news topics, as well as the forecasts, are updated monthly at conflictforecast.org and provide coverage for more than 170 countries and about 65,000 grid cells of size 55 × 55 km worldwide. The forecasts rely on natural language processing (NLP) and machine learning techniques to leverage a large corpus of newspaper text for predicting sudden onsets of violence in peaceful countries. Our goals are a) to support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) to facilitate additional research that can utilize risk forecasts for causal identification, and c) to provide an overview of the news landscape.

Keywords: Civil War, Forecasting, Machine Learning, News Topics, Random Forest, Topic Models

Author links: Christopher Rauh  

Publisher's Link: https://doi.org/10.1017/dap.2024.10

Cambridge Working Paper in Economics Version of Paper: Introducing a Global Dataset on Conflict Forecasts and News Topics, Mueller, H., Rauh, C., Seimon, B. , (2024)