  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.
