一种基于无监督密度的聚类算法检测选举异常:来自乔治亚州最大县的证据

Khurram Yamin, Matthew Oswald, Nima Jadali, Yao Xie, E. Zegura, D. Nazzal
{"title":"一种基于无监督密度的聚类算法检测选举异常:来自乔治亚州最大县的证据","authors":"Khurram Yamin, Matthew Oswald, Nima Jadali, Yao Xie, E. Zegura, D. Nazzal","doi":"10.1145/3530190.3534799","DOIUrl":null,"url":null,"abstract":"The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Unsupervised Density Based Clustering Algorithm to Detect Election Anomalies : Evidence from Georgia’s Largest County\",\"authors\":\"Khurram Yamin, Matthew Oswald, Nima Jadali, Yao Xie, E. Zegura, D. Nazzal\",\"doi\":\"10.1145/3530190.3534799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.\",\"PeriodicalId\":257424,\"journal\":{\"name\":\"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3530190.3534799\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3530190.3534799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

2020年的选举充满了欺诈指控。为了应对缺乏一个强大的方法来调查这些指控,我们提出了一个基于多步骤聚类的方法。我们首先解决一个回归问题,找到一组有影响的变量,然后对这些变量进行聚类,得到一组应该具有相似选举结果的选区。重新聚类每个聚类显示我们的异常值。然后,我们将这种方法应用于富尔顿县,这是佐治亚州最大的县,也是腐败和欺诈指控的中心。我们的研究表明,发现的舞弊程度并不严重,不足以改变格鲁吉亚的选举结果。事实上,大多数显示出异常的选区都是特朗普获得比预期更多选票的选区。我们还通过应用于2015年阿根廷全国大选来验证我们的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Unsupervised Density Based Clustering Algorithm to Detect Election Anomalies : Evidence from Georgia’s Largest County
The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
相关文献
二甲双胍通过HDAC6和FoxO3a转录调控肌肉生长抑制素诱导肌肉萎缩
IF 8.9 1区 医学Journal of Cachexia, Sarcopenia and MusclePub Date : 2021-11-02 DOI: 10.1002/jcsm.12833
Min Ju Kang, Ji Wook Moon, Jung Ok Lee, Ji Hae Kim, Eun Jeong Jung, Su Jin Kim, Joo Yeon Oh, Sang Woo Wu, Pu Reum Lee, Sun Hwa Park, Hyeon Soo Kim
具有疾病敏感单倍型的非亲属供体脐带血移植后的1型糖尿病
IF 3.2 3区 医学Journal of Diabetes InvestigationPub Date : 2022-11-02 DOI: 10.1111/jdi.13939
Kensuke Matsumoto, Taisuke Matsuyama, Ritsu Sumiyoshi, Matsuo Takuji, Tadashi Yamamoto, Ryosuke Shirasaki, Haruko Tashiro
封面:蛋白质组学分析确定IRSp53和fastin是PRV输出和直接细胞-细胞传播的关键
IF 3.4 4区 生物学ProteomicsPub Date : 2019-12-02 DOI: 10.1002/pmic.201970201
Fei-Long Yu, Huan Miao, Jinjin Xia, Fan Jia, Huadong Wang, Fuqiang Xu, Lin Guo
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
NOTE: Unavoidable Service to Unnoticeable Risks: A Study on How Healthcare Record Management Opens the Doors of Unnoticeable Vulnerabilities for Rohingya Refugees Making AI Explainable in the Global South: A Systematic Review Note: A Sociomaterial Perspective on Trace Data Collection: Strategies for Democratizing and Limiting Bias Complexity of Factor Analysis for Particulate Matter (PM) Data: A Measurement Based Case Study in Delhi-NCR Note: Urbanization and Literacy as factors in Politicians’ Social Media Use in a largely Rural State: Evidence from Uttar Pradesh, India
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1