End User Accounts of Dark Patterns as Felt Manipulation
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Abstract (arXiv)
Manipulation defines many of our experiences as a consumer, including subtle nudges and overt advertising campaigns that seek to gain our attention and money. With the advent of digital services that can continuously optimize online experiences to favor stakeholder requirements, increasingly designers and developers make use of "dark patterns"---forms of manipulation that prey on human psychology---to encourage certain behaviors and discourage others in ways that present unequal value to the end user. In this paper, we provide an account of end user perceptions of manipulation that builds on and extends notions of dark patterns. We report on the results of a survey of users conducted in English and Mandarin Chinese (n=169), including follow-up interviews from nine survey respondents. We used a card sorting method to support thematic analysis of responses from each cultural context, identifying both qualitatively-supported insights to describe end users' felt experiences of manipulative products, and a continuum of manipulation. We further support this analysis through a quantitative analysis of survey results and the presentation of vignettes from the interviews. We conclude with implications for future research, considerations for public policy, and guidance on how to further empower and give users autonomy in their experiences with digital services.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024Characterizing and modeling harms from interactions with design patterns in AI interfacescited by 11
- 2025SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agentscited by 8
- 2025DarkPatterns-LLM: A Multi-Layer Benchmark for Detecting Manipulative and Harmful AI Behaviorcited by 1
- 2025DarkPatterns-LLM: A Multi-Layer Benchmark for Detecting Manipulative and Harmful AI Behavior27 Dec 2025
- 2025SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents13 Oct 2025
- 2024Characterizing and modeling harms from interactions with design patterns in AI interfaces17 Apr 2024