Bär Dominik, Pröllochs Nicolas, Feuerriegel Stefan
LMU Munich, 80539 Munich, Germany.
Munich Center for Machine Learning, 80539 Munich, Germany.
PNAS Nexus. 2025 Mar 5;4(3):pgaf073. doi: 10.1093/pnasnexus/pgaf073. eCollection 2025 Mar.
Social media ads have become a key communication channel in politics. However, the relationship between political ads from social media and election outcomes is not fully understood. Here, we aim to estimate the association between online political advertising and election outcomes during the 2021 German federal election. For this, we analyze a large-scale dataset of 21,641 political ads from Facebook and Instagram that received ≈126 million impressions. Using regression analysis, we show that political advertising on social media has a positive relationship with a candidate's election outcome and may even sway elections. All else equal, ≈200,000 additional impressions are predicted to increase a candidate's votes by 2.1%. We further use a causal sensitivity analysis to evaluate how unobserved confounding may affect our estimates. We find that the estimated impact of ads cannot be reasonably explained away, highlighting the significance of social media for election outcomes.
社交媒体广告已成为政治领域的关键沟通渠道。然而,社交媒体上的政治广告与选举结果之间的关系尚未得到充分理解。在此,我们旨在估算2021年德国联邦选举期间在线政治广告与选举结果之间的关联。为此,我们分析了来自脸书和照片墙的21641条政治广告的大规模数据集,这些广告获得了约1.26亿次展示量。通过回归分析,我们表明社交媒体上的政治广告与候选人的选举结果呈正相关,甚至可能左右选举。在其他条件相同的情况下,预计约20万次额外展示量会使候选人的选票增加2.1%。我们进一步使用因果敏感性分析来评估未观察到的混杂因素可能如何影响我们的估计。我们发现广告的估计影响无法得到合理的解释,这凸显了社交媒体对选举结果的重要性。