Political advertising on social media has fundamentally reshaped democratic deliberation, playing a central role in electoral campaigns and propaganda. However, its systemic impact remains largely theoretical or unexplored, raising critical concerns about institutional fairness and algorithmic transparency. This paper provides the first data-driven analysis of the relationship between direct democracy and political advertising on social media, leveraging a novel dataset of 40,000 political ads published on Meta in Switzerland between 2021 and 2025. Switzerland's system of direct democracy, characterized by frequent referenda, provides an ideal context for examining this relationship beyond standard electoral cycles. The results reveal the sheer scale of digital campaigning, with 560 million impressions targeting 5.6 million voters, and suggest that greater exposure to "pro-Yes" advertising significantly correlates with referendum approval outcomes. Demographic microtargeting analysis suggests partisan strategies: Centrist and right-wing parties predominantly target older men, whereas left-wing parties focus on young women. Regarding textual content, a clear pattern of "talking past each other" is identified; in line with the issue ownership theory, parties avoid debating shared issues, preferring to promote exclusively owned topics. Furthermore, the parties' strategies are so distinctive that a machine learning model trained only on audience and topic features can accurately predict the author of an advertisement. This article highlights how demographic microtargeting, issue divergence, and tailored messages could undermine democratic deliberation, exposing a paradox: Referenda are designed to be the ultimate expression of the popular will, yet they are highly susceptible to invisible algorithmic persuasion.

翻译：社交媒体上的政治广告从根本上重塑了民主审议，在选举活动和宣传中扮演着核心角色。然而，其系统性影响在很大程度上仍停留在理论层面或未被探索，引发了关于制度公平性与算法透明度的重大关切。本文利用2021年至2025年间在瑞士Meta平台上发布的4万条政治广告构成的新颖数据集，首次对直接民主与社交媒体政治广告之间的关系进行了数据驱动分析。瑞士以频繁公投为特征的直接民主制度，为在标准选举周期之外考察这一关系提供了理想情境。研究结果揭示了数字竞选活动的庞大规模：5.6亿次展示覆盖了560万选民，并表明更多接触“赞成票”广告与公投通过结果显著相关。人口统计学微观定向分析揭示了党派策略：中右翼政党主要针对年长男性，而左翼政党则聚焦于年轻女性。在文本内容方面，研究识别出明显的“各说各话”模式；根据议题所有权理论，各党派避免辩论共享议题，更倾向于推广其独占的话题。此外，各党派的策略差异显著，仅基于受众和议题特征训练的机器学习模型便能准确预测广告发布者。本文强调了人口统计学微观定向、议题分化与定制化信息如何可能削弱民主审议，揭示了一个悖论：公投本意是民众意志的终极表达，却极易受到不可见的算法说服的影响。