Digital Advertising in U.S. Federal Elections, 2004-2020

Adam Sheingate, James Scharf, Conner Delahanty
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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.
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2004-2020年美国联邦选举中的数字广告
数字广告现在是美国政治传播的一个常见特征。之前的研究记录了与数字政治广告相关的关键创新及其对竞选和选举的影响。然而,由于在获取和分析数据方面存在挑战,数字广告上的政治支出的全面情况仍然难以捉摸。我们通过一个独特的数据集(N=3,639,166)来解决这一挑战,该数据集来自2004年至2020年期间向联邦选举委员会(FEC)报告的1300多万份支出记录。利用机器学习模型将支出分为九类,包括数字广告和服务,本文得出了四个关键观察结果。首先,2020年是数字竞选支出增长的分水岭。其次,在分配给数字广告的资源上存在明显的党派差异。第三,平台公司在数字广告和服务的党派市场中扮演着核心角色。第四,数字平台和顾问在两党内部占据着独特的意识形态利基。
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