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Faculty of Economics


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

Introducing a Global Dataset on Conflict Forecasts and News Topics


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 and provide coverage for more than 170 countries and about 65,000 grid cells of size 55x55km 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 to:
a) support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) facilitate additional research that can utilise risk forecasts for causal identification, and to c) provide an overview of the news landscape.

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

Author links: Christopher Rauh  


Open Access Link:

Published Version of Paper: Introducing a Global Dataset on Conflict Forecasts and News Topics, Mueller, H., Rauh, C. and Seimon, B., Data & Policy (2024)

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