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Introducing a Global Dataset on Conflict Forecasts and News Topics

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Listed:
  • Mueller, H.
  • Rauh, C.
  • Seimon, B.

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 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.

Suggested Citation

  • Mueller, H. & Rauh, C. & Seimon, B., 2024. "Introducing a Global Dataset on Conflict Forecasts and News Topics," Cambridge Working Papers in Economics 2404, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2404
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    References listed on IDEAS

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    1. Dominic Rohner & Mathias Thoenig, 2021. "The Elusive Peace Dividend of Development Policy: From War Traps to Macro Complementarities," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 111-131, August.
    2. Håvard Hegre & Paola Vesco & Michael Colaresi, 2022. "Lessons from an escalation prediction competition," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 521-554, July.
    3. 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.
    4. Mueller, H. & Rauh, C. & Ruggieri, A., 2022. "Dynamic Early Warning and Action Model," Cambridge Working Papers in Economics 2236, Faculty of Economics, University of Cambridge.
    5. Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Peck, 2022. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 764-779, October.
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    More about this item

    Keywords

    Civil War; Conflict; Forecasting; Machine Learning; News Topics; Random Forest; Topic Models;
    All these keywords.

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