"Talking past each other": Issue ownership and microtargeting in Swiss online political ads

• URL: http://arxiv.org/abs/2512.14564v1
• Date: Tue, 16 Dec 2025 16:35:02 GMT
• Title: "Talking past each other": Issue ownership and microtargeting in Swiss online political ads
• Authors: Arthur Capozzi,
• Abstract summary: This paper presents a large-scale, data-driven analysis of 40k political ads published on Facebook and Instagram in Switzerland between 2021 and 2025.<n>It shows that political ads are used not only for federal elections, but also to influence referenda, where greater exposure to pro-Yes'' advertising correlates significantly with approval outcomes.
• Score: 0.3384279376065155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Switzerland's unique system of direct democracy, characterized by frequent popular referenda, provides a critical context for studying the impact of online political advertising beyond standard electoral cycles. This paper presents a large-scale, data-driven analysis of 40k political ads published on Facebook and Instagram in Switzerland between 2021 and 2025. Despite a voting population of only 5.6 million, the ad campaigns were significant in scale, costing CHF 4.5 million and achieving 560 million impressions. This study shows that political ads are used not only for federal elections, but also to influence referenda, where greater exposure to ``pro-Yes'' advertising correlates significantly with approval outcomes. The analysis of microtargeting reveals distinct partisan strategies: centrist and right-wing parties predominantly target older men, whereas left-wing parties focus on young women. Furthermore, significant region-specific demographic variations are observed even within the same party, reflecting Switzerland's strong territorial divisions. Regarding content, a clear pattern of ``talking past each other'' is identified: in line with issue ownership theory, parties avoid direct debate on shared issues, preferring to promote exclusively owned topics. Finally, it is demonstrated that these strategies are so distinct that an ad's author can be predicted using a machine learning model trained exclusively on its audience and topic features. This study sheds light on how microtargeting and issue divergence on social platforms may fragment the public sphere and bypass traditional democratic deliberation.

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