A Comprehensive Study on Dark Patterns
Meng Li, Xiang Wang, Liming Nie, Chenglin Li, Yang Liu and, Yangyang Zhao, Lei Xue, Kabir Sulaiman Said

TL;DR
This paper introduces a comprehensive framework and taxonomy for dark patterns, evaluates detection tools and datasets, revealing significant gaps and suggesting directions for improved identification and classification of manipulative UI elements.
Contribution
The study develops the Dark Pattern Analysis Framework and a detailed taxonomy of 68 dark pattern types, and evaluates detection tools and datasets for coverage and effectiveness.
Findings
Detection tools identify only 45.5% of dark pattern types
Datasets cover only 44% of dark pattern types
Unified datasets reveal gaps in current detection capabilities
Abstract
As digital interfaces become increasingly prevalent, certain manipulative design elements have emerged that may harm user interests, raising associated ethical concerns and bringing dark patterns into focus as a significant research topic. Manipulative design strategies are widely used in user interfaces (UI) primarily to guide user behavior in ways that favor service providers, often at the cost of the users themselves. This paper addresses three main challenges in dark pattern research: inconsistencies and incompleteness in classification, limitations of detection tools, and insufficient comprehensiveness in existing datasets. In this study, we propose a comprehensive analytical framework--the Dark Pattern Analysis Framework (DPAF). Using this framework, we developed a taxonomy comprising 68 types of dark patterns, each annotated in detail to illustrate its impact on users, potential…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Color Science and Applications
