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

Hannes Mueller
Authors registered in the RePEc Author Service: Christopher Rauh

No 990, Working Papers from Barcelona School of Economics

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 which makes 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.

Keywords: conflict; Civil War; forecasting; machine learning; early-warning; topic model; news; prediction; panel regression (search for similar items in EconPapers)
JEL-codes: O11 O43 (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-big, nep-pol and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Journal Article: Reading Between the Lines: Prediction of Political Violence Using Newspaper Text (2018)
Working Paper: Reading Between the Lines: Prediction of Political Violence Using Newspaper Text (2016)
Working Paper: Reading Between the Lines: Prediction of Political Violence Using Newspaper Text (2016)
Working Paper: Reading Between the Lines: Prediction of Political Violence Using Newspaper Text (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:990

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