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Using Past Violence and Current News to Predict Changes in Violence

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Abstract

This article proposes a 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.

Suggested Citation

  • Mueller, H. & Rauh, C., 2022. "Using Past Violence and Current News to Predict Changes in Violence," Cambridge Working Papers in Economics 2220, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2220
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    References listed on IDEAS

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    1. Paola Vesco & Håvard Hegre & Michael Colaresi & Remco Bastiaan Jansen & Adeline Lo & Gregor Reisch & Nils B. Weidmann, 2022. "United they stand: Findings from an escalation prediction competition," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 860-896, July.
    2. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    3. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
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    Cited by:

    1. BBVA Research & Alvaro Ortiz & Tomasa Rodrigo, 2025. "Global | Geopolítica, geoeconomía y riesgo: un enfoque basado en aprendizaje automático [Global | Geopolitics, geoeconomics and risk: a machine learning approach]," Working Papers 25/14, BBVA Bank, Economic Research Department.
    2. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    3. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    4. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    5. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
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    JEL classification:

    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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