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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.|
|Citation:||Mathur, Arunesh, Gunes Acar, Michael J. Friedman, Elena Lucherini, Jonathan Mayer, Marshini Chetty, and Arvind Narayanan. "Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): pp. 81:1-81:32. doi:10.1145/3359183|
|Pages:||81:1 - 81:32|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the ACM on Human-Computer Interaction|
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