Online political advertising has become the cornerstone of political campaigns. The budget spent solely on political advertising in the U.S. has increased by more than 100% from \$700 million during the 2017-2018 U.S. election cycle to \$1.6 billion during the 2020 U.S. presidential elections. Naturally, the capacity offered by online platforms to micro-target ads with political content has been worrying lawmakers, journalists, and online platforms, especially after the 2016 U.S. presidential election, where Cambridge Analytica has targeted voters with political ads congruent with their personality To curb such risks, both online platforms and regulators (through the DSA act proposed by the European Commission) have agreed that researchers, journalists, and civil society need to be able to scrutinize the political ads running on large online platforms. Consequently, online platforms such as Meta and Google have implemented Ad Libraries that contain information about all political ads running on their platforms. This is the first step on a long path. Due to the volume of available data, it is impossible to go through these ads manually, and we now need automated methods and tools to assist in the scrutiny of political ads. In this paper, we focus on political ads that are related to policy. Understanding which policies politicians or organizations promote and to whom is essential in determining dishonest representations. This paper proposes automated methods based on pre-trained models to classify ads in 14 main policy groups identified by the Comparative Agenda Project (CAP). We discuss several inherent challenges that arise. Finally, we analyze policy-related ads featured on Meta platforms during the 2022 French presidential elections period.

翻译：在线政治广告已成为政治竞选活动的基石。美国仅政治广告的投入就从2017-2018选举周期的7亿美元增长了超过100%，达到2020年美国总统选举期间的16亿美元。自然，在线平台对政治内容广告进行微定位（micro-target）的能力一直令立法者、记者和在线平台担忧，尤其是在2016年美国总统选举之后——当时剑桥分析公司（Cambridge Analytica）针对选民个性投放了契合其特征的政治广告。为遏制此类风险，在线平台和监管机构（通过欧盟委员会提出的《数字服务法案》DSA）已达成共识：研究人员、记者和公民社会必须能够对大型在线平台上投放的政治广告进行审查。因此，Meta和谷歌等在线平台已实施了广告库（Ad Libraries），其中包含其平台上所有政治广告的信息。这只是漫长道路的第一步。由于可用数据量巨大，手动逐一审查这些广告不可行，我们亟需自动化方法和工具来辅助政治广告的审查。本文聚焦于与政策相关的政治广告。理解政治家或组织推广哪些政策以及向谁推广，对于判定不实陈述至关重要。本文提出基于预训练模型的自动化方法，将广告分类为比较议程项目（CAP）所识别的14个主要政策组别。我们讨论了由此产生的若干固有挑战。最后，我们分析了2022年法国总统选举期间Meta平台上展示的与政策相关的广告。