Mueller, H., Rauh, C.

Using Past Violence and Current News to Predict Changes in Violence

CWPE2220

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.

Keywords: battle deaths, LDA, machine learning, prediction, random forest, topic model, ViEWS prediction competition

JEL Codes: F21 C53 C55

Author links: Christopher Rauh  

PDF: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2220.pdf

Open Access Link: https://doi.org/10.17863/CAM.83983

Published Version of Paper: Using Past Violence and Current News to Predict Changes in Violence, Mueller, H. and Rauh, C. , International Interactions (2022)

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