Abstract
Electoral fraud can be committed along several stages. Different tools have been applied to detect the existence of such undesired actions. One particular undesired activity is that of vote-buying. It can be thought of as an economical influence of a candidate over voters that in other circumstances could have decided to vote for a different candidate, or not to vote at all. Instead, under this influence, some citizens cast their votes for the suspicious candidate. We propose in this contribution that intelligent data analysis tools can be of help in the identification of this undesired behavior. We think of the results obtained in the affected ballots as a mixture of two signals. The first signal is the number of votes for the suspicious candidate, which includes his/her actual supporters and the voters affected by an economic influence. The second mixed signal is the number of citizens that did not vote, which is affected also by the bribes or economic incentives. These assumptions allows us to apply an instance of blind source separation, independent component analysis, in order to reconstruct the original signals, namely, the actual number of voters the candidate may have had and the actual number of no voters. As a case of study we applied the proposed methodology to the case of presidential elections in Mexico in 2012, obtained by analyzing public data. Our results are consistent with the findings of inconsistencies through other electoral forensic means.
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References
Ball, P.: Critical Mass: How One Thing Leads to Another. Farrar, Straus and Giroux, New York (2006)
Galam, S.: Application of statistical physics to politics. Phys. A Stat. Mech. Appl. 274, 132–139 (1999)
Costa Filho, R.N., Almeida, M.P., Moreira, J.E., Andrade Jr., J.S.: Brazilian elections: voting for a424 scaling democracy. Phys. A 322, 698–700 (2003)
Fortunato, S.: Physics peeks into the ballot box. Phys. Today 65(10), 74–76 (2012). doi:10.1063/PT.3.1761
Alvarez, R.M., Hall, T., Hyde, S.: Election Fraud. Detecting and Deterring Electoral Manipulation. Brookings Institution Press, Washington, DC (2012)
Vicente, P., Wantchekon, L.: Clientelism and vote buying: lessons fromfield experiments in African elections. Oxford Rev. Economicpolicy 25(2), 292–305 (2009). doi:10.1093/oxrep/grp018
http://www.guardian.co.uk/world/2012/jul/04/mexico-elections-shadow-pena-nieto. Accessed 20 Dec 2012
Taibo, P.I. II, Poniatowska, E., Diaz-Polanco, H., Mejia-Madrid, F., Vasconcelos, H., Martinez, S., Miguel, P., Ramirez-Cuevas, J., Suarez del Real, J.A.: Fraude 2012. In: Para Leer en Libertad AC (ed.) Movimiento de Regeneracion Nacional (2012)
Ribando Seelke, C.: Mexico 2012 Elections. Congressional Research Service 2012, 7-7500 (2012)
Diekmann, A., Jann, B.: Benford’s Law and Fraud Detection: Facts and Legends (2013)
Deckert, J., Myagkov, M., Ordeshook, P.: Benfords law and the detection of election fraud. Polit. 427 Anal. 19, 245–268 (2011)
Klimek, P., Yegorovb, Y., Hanela, R., Thurner, S.: Statistical detection of systematic election 430 irregularities. In: PNAS 2012, pp. 1–5 (2012)
Vergara, R.: Elección Comprada: el Escándalo Peña Nieto-Soriana. Revista Proceso. 7 de Julio de 2012. http://www.proceso.com.mx/?p=313518. Retrieved on 9 July 2012
Hernández, J.: Astillero, diario La Jornada. Consulted on 8 July 2012. http://www.jornada.unam.mx/2012/06/11/opinion/004o1pol
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Hyvärinen A. Independent component analysis: recent advances. Phil. Trans. Royal Soc. (2011)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999). doi:10.1109/72.761722
Stone, J.: Independent Analysis Component: A Tutorial Introduction. MIT Press, Cambridge (2004)
Choi, S., Cichocki, A., Park, H., Lee, S.Y.: Blind source separation. Neural Inf. Process. Lett. Rev. 6(1), 1–57 (2005)
Levin, I., Pomares, J., Alvarez, R.M.: Using machine learning algorithms to detect election fraud. Alvarez, R.M. (Ed.) Computational Social Science: Discovery and Prediction. Cambridge University Press, Cambridge
Nichter, S.: Vote buying or turnout buying? Machine politics and the secret ballot. Am. Polit. Sci. Rev. 102(1), 19–31 (2008)
Comon, P., Jutten, C.: Handbook of Blind Source Separation. Independent Component Analysis and Applications. Academic Press, Oxford (2010)
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http://www.theguardian.com/world/2012/jul/04/mexico-elections-shadow-pena-nieto
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Neme, A., Neme, O. (2016). Vote Buying Detection via Independent Component Analysis. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_20
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DOI: https://doi.org/10.1007/978-3-319-46349-0_20
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