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

Autor: Bär D; LMU Munich, Munich 80539, Germany.; Munich Center for Machine Learning, Munich 80539, Germany., Pierri F; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy., De Francisci Morales G; CENTAI, Turin 10138, Italy., Feuerriegel S; LMU Munich, Munich 80539, Germany.; Munich Center for Machine Learning, Munich 80539, Germany.
Jazyk: angličtina
Zdroj: PNAS nexus [PNAS Nexus] 2024 Jun 18; Vol. 3 (7), pp. pgae247. Date of Electronic Publication: 2024 Jun 18 (Print Publication: 2024).
DOI: 10.1093/pnasnexus/pgae247
Abstrakt: 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 article, we analyze targeted political advertising on social media via a unique, large-scale dataset of over 80,000 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 favor ads from populists over others.
(© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)
Databáze: MEDLINE
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