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

Published online by Cambridge University Press:  04 January 2017

Francisco Cantú
Department of Political Science, University of California, San Diego, CA 92093
Sebastián M. Saiegh*
Department of Political Science, University of California, San Diego, CA 92093
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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.

Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 


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