# Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites

arXiv: https://arxiv.org/abs/1907.07032 · HTML: https://mlsanity.com/paper/1907.07032/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [Investigating the Impact of Dark Patterns on LLM-Based Web Agents](https://mlsanity.com/paper/2510.18113/) (2025)
- [Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight](https://mlsanity.com/paper/2509.10723/) (2025)
- [Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems](https://mlsanity.com/paper/2308.00868/) (2023)
- [SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents](https://mlsanity.com/paper/2510.11035/) (2025)
- [Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbots](https://mlsanity.com/paper/2409.15436/) (2024)
- [Dark patterns in e-commerce: a dataset and its baseline evaluations](https://mlsanity.com/paper/2211.06543/) (2022)
- [Deceptive Patterns of Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2404.09375/) (2024)
- [The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions](https://mlsanity.com/paper/2602.17185/) (2026)
- [A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making](https://mlsanity.com/paper/2205.00033/) (2022)
- [Targeted and Troublesome: Tracking and Advertising on Children's Websites](https://mlsanity.com/paper/2308.04887/) (2023)
- [Automatically Detecting Online Deceptive Patterns](https://mlsanity.com/paper/2411.07441/) (2024)
- [Super Kawaii Vocalics: Amplifying the "Cute" Factor in Computer Voice](https://mlsanity.com/paper/2507.06235/) (2025)
- [Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review](https://mlsanity.com/paper/2310.16735/) (2023)
- [Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis](https://mlsanity.com/paper/2401.04119/) (2023)
- [Detecting Dark Patterns in User Interfaces Using Logistic Regression and Bag-of-Words Representation](https://mlsanity.com/paper/2412.14187/) (2024)
- [Deception at Scale: Deceptive Designs in 1K LLM-Generated Ecommerce Components](https://mlsanity.com/paper/2502.13499/) (2025)
- [Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms](https://mlsanity.com/paper/2508.13440/) (2025)
- [BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media](https://mlsanity.com/paper/2606.27274/) (2026)
- [When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks](https://mlsanity.com/paper/2610.12823/) (2026)

## Abstract (arXiv)

Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.