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Reading Between the Lines: Prediction of Political Violence Using Newspaper Text

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  • MUELLER, HANNES
  • RAUH, CHRISTOPHER

Abstract

This article provides a new methodology to predict armed conflict by using newspaper text. Through machine learning, vast quantities of newspaper text are reduced to interpretable topics. These topics are then used in panel regressions to predict the onset of conflict. We propose the use of the within-country variation of these topics to predict the timing of conflict. This allows us to avoid the tendency of predicting conflict only in countries where it occurred before. We show that the within-country variation of topics is a good predictor of conflict and becomes particularly useful when risk in previously peaceful countries arises. Two aspects seem to be responsible for these features. Topics provide depth because they consist of changing, long lists of terms that make them able to capture the changing context of conflict. At the same time, topics provide width because they are summaries of the full text, including stabilizing factors.

Suggested Citation

  • 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.
  • Handle: RePEc:cup:apsrev:v:112:y:2018:i:02:p:358-375_00
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    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • H56 - Public Economics - - National Government Expenditures and Related Policies - - - National Security and War

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