  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.
