On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook
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Abstract (arXiv)
Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 2.
10 papers.
- 2021Technical Challenges for Training Fair Neural Networkscited by 20
- 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategycited by 9
- 2022Weakly Supervised Learning for Analyzing Political Campaigns on Facebookcited by 8
- 2023Analysis of Climate Campaigns on Social Media using Bayesian Model Averagingcited by 3
- 2024Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governancecited by 3
- 2025LLM-Generated Ads: From Personalization Parity to Persuasion Superioritycited by 2
- 2024Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systemscited by 1
- 2025Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertisingcited by 1
- 2026Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibilitycited by 1
- 2026Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discoursecited by 0
- 2026Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility3 Mar 2026
- 2026Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse19 Jan 2026
- 2025LLM-Generated Ads: From Personalization Parity to Persuasion Superiority3 Dec 2025
- 2025Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising16 Oct 2025
- 2024Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems21 Aug 2024
- 2024Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance26 Jul 2024
- 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy16 Apr 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
- 2021Technical Challenges for Training Fair Neural Networks12 Feb 2021