Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy
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Abstract (arXiv)
Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Library to collect 2312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier (F1=0.85) reveals a partisan divide among the major Italian political parties, with anti-immigration ads accounting for nearly 15M impressions. Although composing 47.6% of all migration-related ads, anti-immigration ones receive 65.2% of impressions. We estimate that about two thirds of all captured campaigns use some kind of demographic targeting by location, gender, or age. We find sharp divides by age and gender: for instance, anti-immigration ads from major parties are 17% more likely to be seen by a male user than a female. Unlike pro-migration parties, we find that anti-immigration ones reach a similar demographic to their own voters. However their audience change with topic: an ad from anti-immigration parties is 24% more likely to be seen by a male user when the ad speaks about migration, than if it does not. Furthermore, the viewership of such campaigns tends to follow the volume of mainstream news around immigration, supporting the theory that political advertisers try to "ride the wave" of current news. We conclude with policy implications for political communication: since the Facebook Ads Library does not allow to distinguish between advertisers intentions and algorithmic targeting, we argue that more details should be shared by platforms regarding the targeting configuration of socio-political campaigns.
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). First posted on arXiv 16 Mar 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 1.
8 papers.
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- 2024Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMscited by 8
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- 2025SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotatorscited by 4
- 2023Analysis of Climate Campaigns on Social Media using Bayesian Model Averagingcited by 3
- 2025Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertisingcited by 1
- 2026Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discoursecited by 0
- 2026Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse19 Jan 2026
- 2025Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising16 Oct 2025
- 2025SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators10 Feb 2025
- 2024Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation7 Oct 2024
- 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy16 Apr 2024
- 2024Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs15 Mar 2024
- 2023Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging6 May 2023
- 2022Weakly Supervised Learning for Analyzing Political Campaigns on Facebook19 Oct 2022