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Replication data for "Using Past Violence and Current News to Predict Changes in Violence" by Mueller and Rauh (2022)

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for "Using Past Violence and Current News to Predict Changes in Violence" by Mueller and Rauh (2022)
 
Identifier https://doi.org/10.7910/DVN/BW7UV4
 
Creator Rauh, Christopher
 
Publisher Harvard Dataverse
 
Description Replication material for the ViEWS prediction competition entry by Hannes Mueller and Christopher Rauh. The accompanying article for the special issue explains the 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.
 
Subject Social Sciences
 
Contributor Rauh, Christopher