IDEAS home Printed from https://ideas.repec.org/a/cup/pscirm/v9y2021i1p122-139_8.html
   My bibliography  Save this article

We need to go deeper: measuring electoral violence using convolutional neural networks and social media

Author

Listed:
  • Muchlinski, David
  • Yang, Xiao
  • Birch, Sarah
  • Macdonald, Craig
  • Ounis, Iadh

Abstract

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.

Suggested Citation

  • Muchlinski, David & Yang, Xiao & Birch, Sarah & Macdonald, Craig & Ounis, Iadh, 2021. "We need to go deeper: measuring electoral violence using convolutional neural networks and social media," Political Science Research and Methods, Cambridge University Press, vol. 9(1), pages 122-139, January.
    • Handle: RePEc:cup:pscirm:v:9:y:2021:i:1:p:122-139_8
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S2049847020000321/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. John A. Doces, 2024. "Electoral proximity, political violence, and personal wellbeing: An experimental analysis in West Africa," Economics and Politics, Wiley Blackwell, vol. 36(1), pages 373-397, March.
    2. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    3. Sarah Birch & Ursula Daxecker & Kristine Höglund, 2020. "Electoral violence: An introduction," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(1), pages 3-14, January.

    More about this item

    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:cup:pscirm:v:9:y:2021:i:1:p:122-139_8. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ram .

    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.