IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/2220.html
   My bibliography  Save this paper

Using Past Violence and Current News to Predict Changes in Violence

Author

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.

Suggested Citation

  • Mueller, H. & Rauh, C., 2022. "Using Past Violence and Current News to Predict Changes in Violence," Cambridge Working Papers in Economics 2220, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:2220
    Note: cr542
    as

    Download full text from publisher

    File URL: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2220.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    2. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    2. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    3. Mueller, H. & Rauh, C. & Ruggieri, A., 2022. "Dynamic Early Warning and Action Model," Cambridge Working Papers in Economics 2236, Faculty of Economics, University of Cambridge.
    4. Samuel Bazzi & Robert A. Blair & Christopher Blattman & Oeindrila Dube & Matthew Gudgeon & Richard Peck, 2022. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 764-779, October.
    5. Lamprini Rori & Vasiliki Georgiadou & Costas Roumanias, 2022. "Political violence in Greece through the PVGR database: evidence from the far right and the far left," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 167, Hellenic Observatory, LSE.
    6. Luca Gambetti & Nicolò Maffei-Faccioli & Sarah Zoi, 2022. "Bad News, Good News: Coverage and Response Asymmetries," Working Paper 2022/8, Norges Bank.
    7. Nicola Limodio, 2022. "Terrorism Financing, Recruitment, and Attacks," Econometrica, Econometric Society, vol. 90(4), pages 1711-1742, July.
    8. Besley, Timothy & Fetzer, Thiemo & Mueller, Hannes, 2019. "Terror and Tourism: The Economic Consequences of Media Coverage," CAGE Online Working Paper Series 449, Competitive Advantage in the Global Economy (CAGE).
    9. Augustin TAPSOBA, 2022. "Conflict prediction using Kernel density estimation," Working Paper 258fc89a-4ec3-4eef-a0ff-7, Agence française de développement.
    10. Lite J. Nartey & Witold J. Henisz & Sinziana Dorobantu, 2023. "Reciprocity in Firm–Stakeholder Dialog: Timeliness, Valence, Richness, and Topicality," Journal of Business Ethics, Springer, vol. 183(2), pages 429-451, March.
    11. Toke S. Aidt & Facundo Albornoz & Esther Hauk, 2019. "Foreign in influence and domestic policy: A survey," Cambridge Working Papers in Economics 1928, Faculty of Economics, University of Cambridge.
    12. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.
    13. Andres, Maximilian & Bruttel, Lisa & Friedrichsen, Jana, 2023. "How communication makes the difference between a cartel and tacit collusion: A machine learning approach," European Economic Review, Elsevier, vol. 152(C).
    14. Toke S. Aidt & Facundo Albornoz & Esther Hauk, 2021. "Foreign Influence and Domestic Policy," Journal of Economic Literature, American Economic Association, vol. 59(2), pages 426-487, June.
    15. Timothy Besley & Thiemo Fetzer & Hannes Mueller, 2023. "How Big Is the Media Multiplier? Evidence from Dyadic News Data," CESifo Working Paper Series 10619, CESifo.
    16. Besley, Timothy & Fetzer, Thiemo & Mueller, Hannes, 2023. "How Big is the Media Multiplier? Evidence from Dyadic News Data," CAGE Online Working Paper Series 692, Competitive Advantage in the Global Economy (CAGE).
    17. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    18. Amarasinghe, Ashani, 2022. "Diverting domestic turmoil," Journal of Public Economics, Elsevier, vol. 208(C).
    19. Morris, J., 2023. "The Impact of Qualitative Reviews on Racial Statistical Discrimination: Evidence from Airbnb," Cambridge Working Papers in Economics 2331, Faculty of Economics, University of Cambridge.
    20. Dmytro Krukovets, 2020. "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24.

    More about this item

    Keywords

    Conflict; prediction; machine learning; LDA; topic model; battle deaths; ViEWS prediction competition; random forest;
    All these keywords.

    JEL classification:

    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cam:camdae:2220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.