Context: Dark patterns are user-interface design strategies deliberately engineered to coerce, mislead, or manipulate users into performing actions that serve business interests at the expense of user autonomy, privacy, and informed consent. As digital commerce and online services have proliferated, so too has the prevalence of such deceptive design choices, raising urgent ethical, legal, and technical questions. Objective: This paper presents a systematic review of dark patterns across modern web applications, proposes a multi-layered taxonomy, an automated detection framework (Dark Scan), and a severity-scoring model (DPSS). Methods: We employ a mixed-method approach combining automated DOM and behavioural analysis, expert interview synthesis, and controlled user-study evidence from published literature.
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Detection and Analysis of Dark Patterns in Modern Web Applications
The scale and harm of dark patterns demand coordinated responses, and this paper proposes a multi-layered taxonomy, an automated detection framework (Dark Scan), and a severity-scoring model (DPSS) to address this problem.