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

TL;DR

This study automates the detection of dark patterns on 11,000 shopping websites, revealing widespread deceptive practices, categorizing their types, and providing insights for regulation and user protection.

Contribution

We developed automated techniques to identify dark patterns at scale and created a taxonomy to understand their influence and potential harm.

Findings

Identified 1,818 dark pattern instances across 11K websites

Found 183 websites engaging in deceptive practices

Discovered 22 third-party dark pattern solutions

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.