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Dark patterns are user interface design choices that benefit an onlineservice by coercing, steering, or deceiving users into making unintended andpotentially harmful decisions. We present automated techniques that enableexperts to identify dark patterns on a large set of websites. Using thesetechniques, we study shopping websites, which often use dark patterns toinfluence users into making more purchases or disclosing more information thanthey would otherwise. Analyzing ~53K product pages from ~11K shopping websites,we discover 1,818 dark pattern instances, together representing 15 types and 7broader categories. We examine these dark patterns for deceptive practices, andfind 183 websites that engage in such practices. We also uncover 22 third-partyentities that offer dark patterns as a turnkey solution. Finally, we develop ataxonomy of dark pattern characteristics that describes the underlyinginfluence of the dark patterns and their potential harm on userdecision-making. Based on our findings, we make recommendations forstakeholders including researchers and regulators to study, mitigate, andminimize the use of these patterns.

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