Dark patterns are ubiquitous in digital systems, impacting users throughout their journeys on many popular apps and websites. While substantial efforts from the research community in the last five years have led to consolidated taxonomies of dark patterns, including an emerging ontology, most applications of these descriptors have been focused on analysis of static images or as isolated pattern types. In this paper, we present a case study of Amazon Prime's "Iliad Flow" to illustrate the interplay of dark patterns across a user journey, grounded in insights from a US Federal Trade Commission complaint against the company. We use this case study to lay the groundwork for a methodology of Temporal Analysis of Dark Patterns (TADP), including considerations for characterization of individual dark patterns across a user journey, combinatorial effects of multiple dark patterns types, and implications for expert detection and automated detection.

翻译：黑暗模式在数字系统中无处不在，影响用户在众多热门应用和网站上的全程体验。尽管过去五年研究界的重大努力已形成黑暗模式的整合分类体系（包括新兴本体论），但大多数这些描述符的应用仍聚焦于静态图像分析或孤立的模式类型。本文以亚马逊Prime的“伊利亚特流程”为案例，基于美国联邦贸易委员会对该公司投诉中的洞察，阐释黑暗模式在用户旅程中的交互作用。我们利用该案例奠定黑暗模式时间分析（TADP）方法论的基础，包括用户旅程中单个黑暗模式的特征刻画、多种黑暗模式类型的组合效应，以及专家检测与自动检测的启示。