Election forensics: Using machine learning and synthetic data for possible election anomaly detection

Mali Zhang; R. Michael Alvarez; Ines Levin

doi:10.1371/journal.pone.0223950

Loading metrics

Open Access

Peer-reviewed

Research Article

Election forensics: Using machine learning and synthetic data for possible election anomaly detection

Mali Zhang,

Roles Data curation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America
⨯
R. Michael Alvarez,

Roles Conceptualization, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliation Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America

http://orcid.org/0000-0002-8113-4451

⨯
Ines Levin

Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: inelevin@uci.edu

Affiliation Department of Political Science, University of California, Irvine, CA, United States of America
⨯

Election forensics: Using machine learning and synthetic data for possible election anomaly detection

Mali Zhang,
R. Michael Alvarez,
Ines Levin

Published: October 31, 2019
https://doi.org/10.1371/journal.pone.0223950

About the Authors

Mali Zhang

Roles Data curation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America

R. Michael Alvarez

Roles Conceptualization, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliation Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America

http://orcid.org/0000-0002-8113-4451

Ines Levin

Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: inelevin@uci.edu

Affiliation Department of Political Science, University of California, Irvine, CA, United States of America

Competing Interests

The authors have declared that no competing interests exist.

Subject Areas
?

For more information about PLOS Subject Areas, click here.
We want your feedback. Do these Subject Areas make sense for this article? Click the target next to the incorrect Subject Area and let us know. Thanks for your help!

Elections
Is the Subject Area "Elections" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Forensics
Is the Subject Area "Forensics" applicable to this article?

Thanks for your feedback.
Literacy
Is the Subject Area "Literacy" applicable to this article?

Thanks for your feedback.
Supervised machine learning
Is the Subject Area "Supervised machine learning" applicable to this article?

Thanks for your feedback.
Argentina
Is the Subject Area "Argentina" applicable to this article?

Thanks for your feedback.
Urbanization
Is the Subject Area "Urbanization" applicable to this article?

Thanks for your feedback.
Governments
Is the Subject Area "Governments" applicable to this article?

Thanks for your feedback.