Systematic discrepancies in the delivery of political ads on Facebook and Instagram

TL;DR

This study analyzes a large dataset of political ads on Facebook and Instagram during the 2021 German election, revealing widespread targeting, discrepancies in audience reach, and algorithmic biases favoring populist parties.

Contribution

It provides a comprehensive, data-driven analysis of political ad targeting, delivery discrepancies, and algorithmic biases on social media during an election.

Findings

Targeted political ads are common across all parties.

Significant gaps exist between targeted and actual audiences.

Algorithms favor populist parties, increasing their ad reach at lower costs.

Abstract

Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously unavailable, thus raising concerns regarding fairness, accountability, and transparency in the electoral process. In this paper, we analyze targeted political advertising on social media via a unique, large-scale dataset of over 80000 political ads from Meta during the 2021 German federal election, with more than 1.1 billion impressions. For each political ad, our dataset records granular information about targeting strategies, spending, and actual impressions. We then study (i) the prevalence of targeted ads across the political spectrum; (ii) the discrepancies between targeted and actual audiences due to algorithmic ad delivery; and (iii) which targeting strategies on social media attain a wide reach at low cost. We find that targeted ads are prevalent across the entire political spectrum. Moreover, there are considerable discrepancies between targeted and actual audiences, and systematic differences in the reach of political ads (in impressions-per-EUR) among parties, where the algorithm favors ads from populists over others.