Using Past Violence and Current News to Predict Changes in Violence
Hannes Mueller and
Christopher Rauh ()
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
This article proposes a new method for predicting escalations and deâ€ 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 soâ€ called topicâ€ 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: Conflict; prediction; machine learning; LDA; topic model; battle deaths; ViEWS prediction competition; random forest (search for similar items in EconPapers)
JEL-codes: C53 C55 F21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-cmp
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Working Paper: Using Past Violence and Current News to Predict Changes in Violence (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2220
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