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Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning

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  • Cantú, Francisco
  • Saiegh, Sebastián M.

Abstract

In this paper, we introduce an innovative method to diagnose electoral fraud using vote counts. Specifically, we use synthetic data to develop and train a fraud detection prototype. We employ a naive Bayes classifier as our learning algorithm and rely on digital analysis to identify the features that are most informative about class distinctions. To evaluate the detection capability of the classifier, we use authentic data drawn from a novel data set of district-level vote counts in the province of Buenos Aires (Argentina) between 1931 and 1941, a period with a checkered history of fraud. Our results corroborate the validity of our approach: The elections considered to be irregular (legitimate) by most historical accounts are unambiguously classified as fraudulent (clean) by the learner. More generally, our findings demonstrate the feasibility of generating and using synthetic data for training and testing an electoral fraud detection system.

Suggested Citation

  • Cantú, Francisco & Saiegh, Sebastián M., 2011. "Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning," Political Analysis, Cambridge University Press, vol. 19(4), pages 409-433.
  • Handle: RePEc:cup:polals:v:19:y:2011:i:04:p:409-433_01
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    Cited by:

    1. Gamermann, Daniel & Antunes, Felipe Leite, 2018. "Statistical analysis of Brazilian electoral campaigns via Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 171-188.
    2. Casas, Agustín & Díaz, Guillermo & Trindade, André, 2017. "Who monitors the monitor? Effect of party observers on electoral outcomes," Journal of Public Economics, Elsevier, vol. 145(C), pages 136-149.
    3. Rok Spruk, 2019. "The rise and fall of Argentina," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 28(1), pages 1-40, December.
    4. Marius Jula, 2015. "Using R for Identification of Data Inconsistency in Electoral Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 101-108, September.
    5. Peter Klimek & Raúl Jiménez & Manuel Hidalgo & Abraham Hinteregger & Stefan Thurner, 2018. "Forensic analysis of Turkish elections in 2017–2018," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    6. Lopez Santiago, 2023. "There Is Something in the Water: The Effects of a Bad Government on Voter Turnout," Asociación Argentina de Economía Política: Working Papers 4664, Asociación Argentina de Economía Política.

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