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Let's Talk About Socio-Technical Angst: Tracing the History and Evolution of Dark Patterns on Twitter from 2010-2021

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arxiv 2207.10563 v1 pith:CZX3EOI7 submitted 2022-07-21 cs.SI cs.HC

Ikechukwu Obi , Colin M. Gray , Shruthi Sai Chivukula , Ja-Nae Duane , Janna Johns , Matthew Will , Ziqing Li , Thomas Carlock This is my paper · ORCID
classification cs.SIcs.HC
keywords darkpatternsdiscourseevolutiondesignpracticesangstconcept
verification ladder T0 review T1 audit T2 compute T3 formal
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Designers' use of deceptive and manipulative design practices have become increasingly ubiquitous, impacting users' ability to make choices that respect their agency and autonomy. These practices have been popularly defined through the term "dark patterns" which has gained attention from designers, privacy scholars, and more recently, even legal scholars and regulators. The increased interest in the term and underpinnings of dark patterns across a range of sociotechnical practitioners intrigued us to study the evolution of the concept, to potentially speculate the future trajectory of conversations around dark patterns. In this paper, we examine the history and evolution of the Twitter discourse through #darkpatterns from its inception in June 2010 until April 2021, using a combination of quantitative and qualitative methods to describe how this discourse has changed over time. We frame the evolution of this discourse as an emergent transdisciplinary conversation that connects multiple disciplinary perspectives through the shared concept of dark patterns, whereby these participants engage in a conversation marked by socio-technical angst in order to identify and fight back against deceptive design practices. We discuss the potential future trajectories of this discourse and opportunities for further scholarship at the intersection of design, policy, and activism.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications

    cs.CR 2025-01 conditional novelty 6.0 of 10

    A multimodal AI detector, DPGuard, combined with a unified 21-category taxonomy, claims state-of-the-art detection of deceptive UI patterns and finds them in 47% of popular websites and 24% of mobile screenshots.

  2. Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets

    cs.LG 2024-11 conditional novelty 5.0 of 10

    An audit of three RLHF datasets using a new seven-value taxonomy and a classifier shows that information-utility values dominate and prosocial values are underrepresented.

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