Mining Voter Behaviour and Confidence: A Rule-Based Analysis of the 2022 U.S. Elections
Md Al Jubair, Mohammad Shamsul Arefin, Ahmed Wasif Reza

TL;DR
This paper uses rule-based data mining on survey data from the 2022 U.S. elections to uncover how demographic factors and voting experiences influence voter trust and confidence.
Contribution
It introduces a novel application of the Apriori algorithm to analyze election survey data, revealing key associations between voter experiences and trust levels.
Findings
Voters with easy access and moderate confidence are over six times more likely to trust election results.
98.16% of Black voters with easy access reported smooth registration experiences.
High confidence correlates with increased likelihood of identifying as Democratic.
Abstract
This study explores the relationship between voter trust and their experiences during elections by applying a rule-based data mining technique to the 2022 Survey of the Performance of American Elections (SPAE). Using the Apriori algorithm and setting parameters to capture meaningful associations (support >= 3%, confidence >= 60%, and lift > 1.5), the analysis revealed a strong connection between demographic attributes and voting-related challenges, such as registration hurdles, accessibility issues, and queue times. For instance, respondents who indicated that accessing polling stations was "very easy" and who reported moderate confidence were found to be over six times more likely (lift = 6.12) to trust their county's election outcome and experience no registration issues. A further analysis, which adjusted the support threshold to 2%, specifically examined patterns among minority…
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