In this article, we explore both unintentional and intentional anomalies that may arise in real voter registration data from a U.S. state. Through our collaborative efforts with the Idaho Secretary of State office, we identify and characterize various anomalies such as missing values in the required fields, abnormal age entries, unspecified gender types, non-unique driver’s license numbers, and formatting errors. Additionally, we present techniques, including a tailored approximate string matching algorithm, capable of detecting potential intentional anomalies in the real data. Gaining a comprehensive understanding of these anomalies is crucial for ensuring the integrity and accuracy of voter registration data. Therefore, we have developed software, in ongoing partnership with the Idaho Secretary of State office, that successfully identifies many of the anomalies. This software-based approach has proven effective and can be adapted for use in other states.
AnsolabehereStephen and HershEitan D.. 2017. “ADGN: An algorithm for record linkage using address, date of birth, gender, and name.” Statistics and Public Policy, 4(1):1–10.
BaptistaJoão Pedro and GradimAnabela. 2022. “Online disinformation on Facebook: The spread of fake news during the Portuguese 2019 election.” Journal of Contemporary European Studies, 30(2):297–312; 10.1080/14782804.2020.1843415
6.
BerlinskiNicolas, DoyleMargaret, GuessAndrew M., LevyGabrielle, LyonsBenjamin, MontgomeryJacob M., NyhanBrendan, and ReiflerJason. 2023. “The effects of unsubstantiated claims of voter fraud on confidence in elections.” Journal of Experimental Political Science, 10(1):34–49.
BrodaElena and StrömbäckJesper. 2024. “Misinformation, disinformation, and fake news: Lessons from an interdisciplinary, systematic literature review.” Annals of the International Communication Association, 48(2):139–166; 10.1080/23808985.2024.2323736
9.
CaoJian, KimSeo-Young Silvia, and AlvarezR. Michael. 2022. “Bayesian analysis of state voter registration database integrity.” Statistics, Politics and Policy, 13(1):19–40.
10.
ChristenPeter. 2012. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection.Springer Publishing Company, Incorporated.
11.
Electronic Registration Information Center. 2024. Statistics. Available from: https://ericstates.org/statistics/ [Last accessed: November20, 2024].
12.
EnamoradoTed., FifieldBenjamin., and ImaiKosuke.. 2019. “Using a probabilistic model to assist merging of large-scale administrative records.” American Political Science Review, 113(2):353–371.
13.
FragaBernard L, PeskowitzZachary, and SzewczykJames. 2024. Can elite allegations of election fraud demobilize supporters? Political Behavior. Springer, 1–25.
GerbinaT. V. 2021. “Science disinformation: On the problem of fake news.” Scientific and Technical Information Processing, 48(4):290–298; 10.3103/S0147688221040092
16.
GoelSharad., MeredithMarc., MorseMichael., RothschildDavid., and Shirani-MehrHoushmand.. 2020. “One person, one vote: Estimating the prevalence of double voting in US presidential elections.” American Political Science Review, 114(2):456–469.
17.
GroarkeMargaret. 2016. “The impact of voter fraud claims on voter registration reform legislation.” Political Science Quarterly, 131(3):571–595; doi: 10.1002/polq.12488
18.
KabraNavin and GoyalPankaj. 1998. Efficient fuzzy matching of strings. In Proceedings of the 24th international conference on very large data bases (pp. 557–569).
19.
KalyanathayaKrishna and., AkilaD., and SuseendranG.. 2019. “A Fuzzy approach to approximate string matching for text retrieval in NLP.” Journal of Computational Information Systems, 15:26–32.
20.
KimSeo-Young Silvia, SchneiderSpencer, and AlvarezR. Michael. 2020. “Evaluating the quality of changes in voter registration databases: Part of special symposium on election sciences.” American Politics Research, 48(6):670–676.
21.
Leskovec, RajaramanJure, UllmanAnand, and DavidJeffrey. 2020. Mining of Massive Data Sets. Cambridge university press.
22.
LevittJustin. (2007). The truth about voter fraud. 10.2139/ssrn.1647224
NavarroGonzalo. 2001. “A guided tour to approximate string matching.” ACM Computing Surveys, 33(1):31–88.
29.
Parker, MehrpouyanKamryn, Hoda, KettlerJaclyn, and HouckChad. 2022. Connecting incident reporting infrastructure to election day proceedings. In European Symposium on Research in Computer Security, 568–584, Springer.
PearsonKarl. 1900. “X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302):157–175; 10.1080/14786440009463897
32.
RoystonSam, GreenbergBen, TavasoliOmeed, and CottonCourtenay. 2021. “Anomaly detection and automated labeling for voter registration file changes.” arXiv Preprint arXiv:2106.15285.
33.
ShantonKaren L. 2019. State and Local Role in Election Administration: Duties and Structures.
34.
The Heritage Foundation. 2024. Election Fraud Database. Available from: https://electionfraud.heritage.org/ [Last accessed: December22, 2024].
35.
UkkonenEsko. 1992. “Approximate string-matching with q-grams and maximal matches.” Theoretical Computer Science, 92(1):191–211.
