论文标题
算法如何塑造政治广告的分布:Facebook,Google和Tiktok的案例研究
How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok
论文作者
论文摘要
在线平台通过影响选民的政治信息分配来塑造民主的角色越来越重要。近年来,政治运动已经在平台的算法工具上花费了大量时间来针对选民的在线广告。尽管公众对平台如何执行塑造政治话语的任务的兴趣从未有所更高,但主要平台为理解其实践所必需的披露而做出的努力差不多。在这项研究中,我们从Facebook,Google和Tiktok收集和分析包含有关2020年美国总统大选的数据集,其中包含超过80万广告和250万个视频。我们对公共数据进行了第一个大规模数据分析,以严格评估这些平台如何放大或调节政治广告的分布。我们最终提出了有关如何改善披露的建议,以便公众可以使平台和政治广告商负责。
Online platforms play an increasingly important role in shaping democracy by influencing the distribution of political information to the electorate. In recent years, political campaigns have spent heavily on the platforms' algorithmic tools to target voters with online advertising. While the public interest in understanding how platforms perform the task of shaping the political discourse has never been higher, the efforts of the major platforms to make the necessary disclosures to understand their practices falls woefully short. In this study, we collect and analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.