{"title":"Digital Advertising in U.S. Federal Elections, 2004-2020","authors":"Adam Sheingate, James Scharf, Conner Delahanty","doi":"10.51685/jqd.2022.026","DOIUrl":null,"url":null,"abstract":"Digital advertising is now a commonplace feature of political communication in the United States. Previous research has documented the key innovations associated with digital political advertising and its consequences for campaigns and elections. However, a comprehensive picture of political spending on digital advertising remains elusive because of the challenges associated with accessing and analyzing data. We address this challenge with a unique dataset (N=3,639,166) derived from over 13 million expenditure records reported to the Federal Election Commission (FEC) between 2004 and 2020. Employing a machine learning model to classify expenditures into nine categories including digital ads and services, this paper makes four key observations. First, 2020 was a watershed election in the growth of digital campaign spending. Second, there are clear partisan differences in the resources allocated to digital advertising. Third, platform companies play a central role in an otherwise partisan market for digital ads and services. Fourth, digital platforms and consultants occupy a distinct ideological niche within each party.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"225 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of quantitative description: digital media","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51685/jqd.2022.026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Digital advertising is now a commonplace feature of political communication in the United States. Previous research has documented the key innovations associated with digital political advertising and its consequences for campaigns and elections. However, a comprehensive picture of political spending on digital advertising remains elusive because of the challenges associated with accessing and analyzing data. We address this challenge with a unique dataset (N=3,639,166) derived from over 13 million expenditure records reported to the Federal Election Commission (FEC) between 2004 and 2020. Employing a machine learning model to classify expenditures into nine categories including digital ads and services, this paper makes four key observations. First, 2020 was a watershed election in the growth of digital campaign spending. Second, there are clear partisan differences in the resources allocated to digital advertising. Third, platform companies play a central role in an otherwise partisan market for digital ads and services. Fourth, digital platforms and consultants occupy a distinct ideological niche within each party.