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

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Mathur, Arunesh, Acar, Gunes, Friedman, Michael J., Lucherini, Eli, Mayer, Jonathan, Chetty, Marshini, Narayanan, Arvind·2019·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3359183
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Abstract

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.

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  • These dark patterns are especially common in areas where firms and the public have competing incentives, including price discrimination and regulatory compliance.

    “These dark patterns are especially common in areas where firms and the public have competing incentives, including price discrimination and regulatory compliance [40].”

    From Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability (2024) · cites this paper as [40]

    Section 6.2 (pp. 25–26) reports dark patterns used to influence consumers for retailers’ profit and discusses practices that may violate consumer-protection rules. It does not identify price discrimination as an example or establish that dark patterns are especially common where firms’ and the public’s incentives compete. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Detection alone is insufficient to minimize the impact of dark patterns.

    “Although there are efforts to automatically detect dark patterns [32], detection alone is insufficient to minimize their impact.”

    From Investigating the Impact of Dark Patterns on LLM-Based Web Agents (2026) · cites this paper as [32]

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Research works printed but not linked: 42

  1. [25][25] Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, and Claudia Diaz. 2014. The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS '14) . ACM, New York, NY, USA, 674-689. https://doi.org/10.1145/ 2660267.2660347 -
  2. [27][27] Praveen Aggarwal and Rajiv Vaidyanathan. 2015. Use It Or Lose It: Time-Limited Promotions And Purchase Behavior. In Proceedings of the 2002 Academy of Marketing Science (AMS) Annual Conference , Harlan E. Spotts (Ed.). Springer International Publishing, Cham, 2-2. -
  3. [29][29] Hal R Arkes and Peter Ayton. 1999. The sunk cost and Concorde effects: Are humans less rational than lower animals? Psychological bulletin 125, 5 (1999), 591. -
  4. [30][30] Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python (1st ed.). O'Reilly Media, Inc. -
  5. [31][31] Christoph Bösch, Benjamin Erb, Frank Kargl, Henning Kopp, and Stefan Pfattheicher. 2016. Tales from the dark side: Privacy dark strategies and privacy dark patterns. Proceedings on Privacy Enhancing Technologies 2016, 4 (2016), 237-254. -
  6. [34][34] Ryan Calo. 2013. Digital market manipulation. Geo. Wash. L. Rev. 82 (2013), 995. -
  7. [36][36] Shruthi Sai Chivukula, Chris Watkins, Lucca McKay, and Colin M. Gray. 2019. "Nothing Comes Before Profit": Asshole Design In the Wild. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19) . ACM, New York, NY, USA, Article LBW1314, 6 pages. https://doi.org/10.1145/3290607.3312863 - · DOI
  8. [37][37] Robert B Cialdini. 2009. Influence: Science and practice . Vol. 4. Pearson education Boston. -
  9. [38][38] Gregory Conti and Edward Sobiesk. 2010. Malicious interface design: exploiting the user. In Proceedings of the 19th international conference on World wide web . ACM, 271-280. -
  10. [39][39] Biplab Deka, Zifeng Huang, Chad Franzen, Joshua Hibschman, Daniel Afergan, Yang Li, Jeffrey Nichols, and Ranjitha Kumar. 2017. Rico: A Mobile App Dataset for Building Data-Driven Design Applications. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (UIST '17) . ACM, New York, NY, USA, 845-854. https://doi.org/10.1145/3126594.3126651 - · DOI · Paper page
  11. [40][40] Steven Englehardt and Arvind Narayanan. 2016. Online Tracking: A 1-million-site Measurement and Analysis. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16) . ACM, New York, NY, USA, 1388-1401. https://doi.org/10.1145/2976749.2978313 - · DOI
  12. [48][48] Colin M. Gray, Yubo Kou, Bryan Battles, Joseph Hoggatt, and Austin L. Toombs. 2018. The Dark (Patterns) Side of UX Design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18) . ACM, New York, NY, USA, Article 534, 14 pages. https://doi.org/10.1145/3173574.3174108 - · DOI
  13. [49][49] Saul Greenberg, Sebastian Boring, Jo Vermeulen, and Jakub Dostal. 2014. Dark Patterns in Proxemic Interactions: A Critical Perspective. In Proceedings of the 2014 Conference on Designing Interactive Systems (DIS '14) . ACM, New York, NY, USA, 523-532. https://doi.org/10.1145/2598510.2598541 - · DOI
  14. [50][50] Jon D Hanson and Douglas A Kysar. 1999. Taking behavioralism seriously: The problem of market manipulation. NYUL Rev. 74 (1999), 630. -
  15. [52][52] Joel Huber, John W. Payne, and Christopher Puto. 1982. Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis. Journal of Consumer Research 9, 1 (1982), 90-98. http://www.jstor.org/ stable/2488940 -
  16. [53][53] J. Jeffrey Inman and Leigh McAlister. 1994. Do Coupon Expiration Dates Affect Consumer Behavior? Journal of Marketing Research 31, 3 (1994), 423-428. http://www.jstor.org/stable/3152229 -
  17. [55][55] Jae Min Jung and James Kellaris. 2004. Cross-national differences in proneness to scarcity effects: The moderating roles of familiarity, uncertainty avoidance, and need for cognitive closure. Psychology and Marketing 21 (09 2004), 739 - 753. https://doi.org/10.1002/mar.20027 - · DOI
  18. [56][56] Ranjitha Kumar, Arvind Satyanarayan, Cesar Torres, Maxine Lim, Salman Ahmad, Scott R. Klemmer, and Jerry O. Talton. 2013. Webzeitgeist: Design Mining the Web. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13) . ACM, New York, NY, USA, 3083-3092. https://doi.org/10.1145/2470654.2466420 - · DOI
  19. [57][57] Chris Lewis. 2014. Irresistible Apps: Motivational Design Patterns for Apps, Games, and Web-based Communities (1st ed.). Apress, Berkely, CA, USA. -
  20. [58][58] Brian Lickel, Kostadin Kushlev, Victoria Savalei, Shashi Matta, and Toni Schmader. 2014. Shame and the motivation to change the self. Emotion 14, 6 (2014), 1049. -
  21. [59][59] Thomas F. Liu, Mark Craft, Jason Situ, Ersin Yumer, Radomir Mech, and Ranjitha Kumar. 2018. Learning Design Semantics for Mobile Apps. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (UIST '18) . ACM, New York, NY, USA, 569-579. https://doi.org/10.1145/3242587.3242650 - · DOI
  22. [60][60] Sheng Luo, Bin Gu, Xingbiao Wang, and Zhaoquan Zhou. 2018. Online Compulsive Buying Behavior: The Mediating Role of Self-control and Negative Emotions. In Proceedings of the 2018 International Conference on Internet and e-Business . ACM, 65-69. -
  23. [61][61] Xueming Luo. 2005. How Does Shopping With Others Influence Impulsive Purchasing? Journal of Consumer Psychology 15 (12 2005), 288-294. https://doi.org/10.1207/s15327663jcp1504\_3 - · DOI
  24. [62][62] Michael Lynn. 1991. Scarcity effects on value: A quantitative review of the commodity theory literature. Psychology & Marketing 8, 1 (1991), 43-57. -
  25. [63][63] Arunesh Mathur, Arvind Narayanan, and Marshini Chetty. 2018. Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 119 (Nov. 2018), 26 pages. https://doi.org/10.1145/3274388 - · DOI
  26. [64][64] Luigi Mittone and Lucia Savadori. 2009. The scarcity bias. Applied Psychology 58, 3 (2009), 453-468. -
  27. [65][65] Carol Moser, Sarita Y. Schoenebeck, and Paul Resnick. 2019. Impulse Buying: Design Practices and Consumer Needs. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19) . ACM, New York, NY, USA, Article 242, 15 pages. https://doi.org/10.1145/3290605.3300472 - · DOI
  28. [68][68] Nick Nikiforakis, Luca Invernizzi, Alexandros Kapravelos, Steven Van Acker, Wouter Joosen, Christopher Kruegel, Frank Piessens, and Giovanni Vigna. 2012. You Are What You Include: Large-scale Evaluation of Remote Javascript Inclusions. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS '12) . ACM, New York, NY, USA, 736-747. https://doi.org/10.1145/2382196.2382274 - · DOI
  29. [69][69] Chris Nodder. 2013. Evil by design: Interaction design to lead us into temptation . John Wiley & Sons. -
  30. [70][70] C. Whan Park, Sung Youl Jun, and Deborah J. MacInnis. 2000. Choosing What I Want versus Rejecting What I Do Not Want: An Application of Decision Framing to Product Option Choice Decisions. Journal of Marketing Research 37, 2 (2000), 187-202. http://www.jstor.org/stable/1558499 -
  31. [71][71] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825-2830. -
  32. [72][72] Steven Reiss. 2004. Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires. Review of General Psychology 8, 3 (2004), 179-193. https://doi.org/10.1037/1089-2680.8.3.179 arXiv:https://doi.org/10.1037/1089-2680.8.3.179 - · DOI
  33. [73][73] Quirin Scheitle, Oliver Hohlfeld, Julien Gamba, Jonas Jelten, Torsten Zimmermann, Stephen D. Strowes, and Narseo Vallina-Rodriguez. 2018. A Long Way to the Top: Significance, Structure, and Stability of Internet Top Lists. In Proceedings of the Internet Measurement Conference 2018 (IMC '18) . ACM, New York, NY, USA, 478-493. https: //doi.org/10.1145/3278532.3278574 - · DOI
  34. [74][74] Natasha Dow Schüll. 2014. Addiction by design: Machine gambling in Las Vegas . Princeton University Press. -
  35. [77][77] Daniel Susser, Beate Roessler, and Helen Nissenbaum. 2018. Online Manipulation: Hidden Influences in a Digital World. Available at SSRN 3306006 (2018). -
  36. [78][78] Richard H. Thaler and Cass R. Sunstein. 2003. Libertarian Paternalism. American Economic Review 93, 2 (May 2003), 175-179. https://doi.org/10.1257/000282803321947001 - · DOI · Paper page
  37. [79][79] Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science 185, 4157 (1974), 1124-1131. -
  38. [80][80] Amos Tversky and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. Science 211, 4481 (1981), 453-458. arXiv:http://science.sciencemag.org/content/211/4481/453.full.pdf http://science.sciencemag.org/ content/211/4481/453 -
  39. [81][81] Amos Tversky and Daniel Kahneman. 1989. Rational Choice and the Framing of Decisions. In Multiple Criteria Decision Making and Risk Analysis Using Microcomputers , Birsen Karpak and Stanley Zionts (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 81-126. -
  40. [83][83] T. Martin Wilkinson. 2013. Nudging and manipulation. Political Studies 61, 2 (2013), 341-355. -
  41. [84][84] Bo Xiao and Izak Benbasat. 2011. Product-Related Deception in E-Commerce: A Theoretical Perspective. MIS Quarterly 35, 1 (2011), 169-195. http://www.jstor.org/stable/23043494 -
  42. [86][86] Fuzheng Zhang, Nicholas Jing Yuan, Kai Zheng, Defu Lian, Xing Xie, and Yong Rui. 2015. Mining consumer impulsivity from offline and online behavior. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing . ACM, 1281-1292.

Cited by

Sorted by citations

Authors
Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A.
Affiliations
  • Princeton University (US)
  • Princeton University, Princeton, NJ, USA
  • University of Chicago (US)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2019-11-07
Volume
3
Issue
CSCW
Pages
1-32
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3359183
OpenAlex
W4288086176
Cited by
816
References
3
Citations by year
2026: 192 · 2025: 235 · 2024: 111 · 2023: 109 · 2022: 81 · 2021: 56 · 2020: 28 · 2019: 4
FWCI
39.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
  • Lirias (acceptedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
dark patterns, user interface design, deceptive practices, online shopping websites, user decision-making, e-commerce

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