Using Past Violence and Current News to Predict Changes in Violence
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Authors
Mueller, H.
Rauh, C.
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.
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Keywords
Conflict, prediction, machine learning, LDA, topic model, battle deaths, ViEWS prediction competition, random forest
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Faculty of Economics, University of Cambridge