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

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  • 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
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    Cited by:

    1. 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.
    2. 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.

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