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Cantu, F and Saiegh, SM (2011)

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

Political Analysis 19 (4), pp. 409-433.

ISSN/ISBN: Not available at this time. DOI: 10.1093/pan/mpr033

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.

@article{, title={Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning}, volume={19}, DOI={10.1093/pan/mpr033}, number={4}, journal={Political Analysis}, publisher={Cambridge University Press}, author={Cantú, Francisco and Saiegh, Sebastián M.}, pages={409–433}, }

Reference Type: Journal Article

Subject Area(s): Voting Fraud