Literature DB >> 30527668

Election Forensics: Quantitative methods for electoral fraud detection.

Lucas Lacasa1, Juan Fernández-Gracia2.   

Abstract

The last decade has witnessed an explosion on the computational power and a parallel increase of the access to large sets of data - the so called Big Data paradigm - which is enabling to develop brand new quantitative strategies underpinning description, understanding and control of complex scenarios. One interesting area of application concerns fraud detection from online data, and more particularly extracting meaningful information from massive digital fingerprints of electoral activity to detect, a posteriori, evidence of fraudulent behavior. In this short article we discuss a few quantitative methodologies that have emerged in recent years on this respect, which altogether form the nascent interdisciplinary field of election forensics. Aiming to foster discussion and raise awareness on this interdisciplinary area, we hereby enumerate a few of the most relevant approaches and methods.
Copyright © 2018 Elsevier B.V. All rights reserved.

Keywords:  Benford's law; Election forensics; Fraud detection

Year:  2018        PMID: 30527668     DOI: 10.1016/j.forsciint.2018.11.010

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  2 in total

1.  Newcomb-Benford law helps customs officers to detect fraud in international trade.

Authors:  Lucas Lacasa
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-12       Impact factor: 11.205

2.  Reliability of Financial Information from the Perspective of Benford's Law.

Authors:  Ionel Jianu; Iulia Jianu
Journal:  Entropy (Basel)       Date:  2021-04-30       Impact factor: 2.524

  2 in total

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