Political Methods: Computational eJournal最新文献_第2页

Advocacy 2.0: An Analysis of How Advocacy Groups in the United States Perceive and Use Social Media as Tools for Facilitating Civic Engagement and Collective Action 倡导2.0:美国倡导团体如何看待和使用社交媒体作为促进公民参与和集体行动的工具的分析

Political Methods: Computational eJournal Pub Date : 2011-11-08 DOI: 10.2139/SSRN.1956352

Jonathan A. Obar, Paul Zube, Cliff Lampe

引用次数: 303

Imperfect Monitoring in International Trade Cooperation 国际贸易合作监管不完善

Political Methods: Computational eJournal Pub Date : 2011-02-18 DOI: 10.2139/ssrn.1763876

J. Gray, Rene Lindstaedt, Jonathan B. Slapin

引用次数: 1

Networks, Collective Action, and State Formation 网络、集体行动和国家形成

Political Methods: Computational eJournal Pub Date : 2010-12-15 DOI: 10.2139/ssrn.1726041

D. Conway

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引用次数: 0

A Supervised Machine Learning Procedure to Detect Electoral Fraud Using Digital Analysis 使用数字分析检测选举舞弊的监督机器学习程序

Political Methods: Computational eJournal Pub Date : 2010-04-22 DOI: 10.2139/ssrn.1594406

Francisco Cantú, Sebastián Saiegh

{"title":"A Supervised Machine Learning Procedure to Detect Electoral Fraud Using Digital Analysis","authors":"Francisco Cantú, Sebastián Saiegh","doi":"10.2139/ssrn.1594406","DOIUrl":"https://doi.org/10.2139/ssrn.1594406","url":null,"abstract":"This paper introduces a naive Bayes classifier to detect electoral fraud using digit patterns in vote counts with authentic and synthetic data. The procedure is the following: (1) we create 10,000 simulated electoral contests between two parties using Monte Carlo methods. This training set is composed of two disjoint subsets: one containing electoral returns that follow a Benford distribution, and another where the vote counts are purposively \"manipulated\" by electoral tampering – a percentage of votes are taken away from one party and given to the other; (2) we calibrate membership values of the simulated elections (i.e. clean or fraudulent) using logistic regression; (3) we recover class-conditional densities using the relative frequencies from the training set; (4) we apply Bayes' rule to class-conditional probabilities and class priors to establish the membership probabilities of authentic observations. To illustrate our technique, we examine elections in the province of Buenos Aires (Argentina) between 1932 and 1942, a period with a checkered history of fraud. Our analysis allows us to successfully classify electoral contests according to their degree of fraud. More generally, our findings indicate that Benford's Law is an effective tool for identifying fraud, even when minimal information (i.e. electoral returns) is available.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127999805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A New Non-Parametric Matching Method for Bias Adjustment with Applications to Economic Evaluations 一种新的非参数匹配偏差调整方法及其在经济评价中的应用

Political Methods: Computational eJournal Pub Date : 2008-05-11 DOI: 10.2139/ssrn.1138926

J. Sekhon, R. Grieve

引用次数: 24