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Election Forensics: Quantitative methods for electoral fraud detection.

Lucas Lacasa¹, Juan Fernández-Gracia²

¹School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London E14NS, UK.

Forensic Science International

|December 12, 2018

Summary

This summary is machine-generated.

Quantitative methods analyze Big Data from electoral activity to detect fraud. This emerging field, election forensics, uses computational power to uncover fraudulent behavior from digital evidence.

Keywords:

Benford's law Election forensics Fraud detection

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Area of Science:

Computational Social Science
Data Science
Political Science

Background:

The Big Data paradigm offers new quantitative strategies for complex systems.
Online data and massive digital electoral activity provide rich information sources.
Fraud detection is a critical application area for Big Data analytics.

Purpose of the Study:

To introduce the nascent interdisciplinary field of election forensics.
To discuss quantitative methodologies for detecting electoral fraud.
To foster discussion and raise awareness on election forensics.

Main Methods:

Analysis of large datasets ('Big Data') from electoral activities.
Application of quantitative strategies for pattern recognition.
Development of novel computational methodologies for fraud detection.

Main Results:

Emergence of election forensics as an interdisciplinary field.
Identification of key quantitative methodologies for analyzing electoral data.
Demonstration of computational approaches for a posteriori fraud detection.

Conclusions:

Computational power and Big Data enable advanced fraud detection in elections.
Election forensics offers a quantitative framework for understanding electoral integrity.
Further research and discussion are needed to advance this interdisciplinary area.