The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating
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Abstract (arXiv)
Targeted advertising can harm vulnerable groups when it targets individuals' personal and psychological vulnerabilities. We focus on how targeted weight-loss advertisements harm people with histories of disordered eating. We identify three features of targeted advertising that cause harm: the persistence of personal data that can expose vulnerabilities, over-simplifying algorithmic relevancy models, and design patterns encouraging engagement that can facilitate unhealthy behavior. Through a series of semi-structured interviews with individuals with histories of unhealthy body stigma, dieting, and disordered eating, we found that targeted weight-loss ads reinforced low self-esteem and deepened pre-existing anxieties around food and exercise. At the same time, we observed that targeted individuals demonstrated agency and resistance against distressing ads. Drawing on scholarship in postcolonial environmental studies, we use the concept of slow violence to articulate how online targeted advertising inflicts harms that may not be immediately identifiable. CAUTION: This paper includes media that could be triggering, particularly to people with an eating disorder. Please use caution when reading, printing, or disseminating this paper.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2022.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1.
5 papers.
- 2024Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recoverycited by 8
- 2023Targeted and Troublesome: Tracking and Advertising on Children's Websitescited by 1
- 2024Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systemscited by 1
- 2025From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorderscited by 1
- 2024From Exploration to Revelation: Detecting Dark Patterns in Mobile Appscited by 0
- 2025From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders4 Dec 2025
- 2024Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery16 Dec 2024
- 2024From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps27 Nov 2024
- 2024Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems21 Aug 2024
- 2023Targeted and Troublesome: Tracking and Advertising on Children's Websites9 Aug 2023