On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election

TL;DR

This paper develops automated classification methods for policy-related political ads on Meta during the 2022 French elections, addressing challenges in scrutinizing large-scale ad data to enhance transparency and accountability.

Contribution

It introduces automated classification techniques using pre-trained models for policy ads and provides an exploratory analysis of Meta's political ads during the 2022 French elections.

Findings

Classified ads into 14 policy groups with high accuracy

Identified key policy topics promoted during the election

Highlighted challenges in automated ad scrutiny

Abstract

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.