STM Journals Peer-reviewed research from Consortium e-Learning Network

E-Commerce for Future & Trends Review Article

IntelliGaurd WebScan: Uncovering Dark Patterns on E-Commerce Websites

  1. Yashasvi Department of Computer Science, Hansraj College, University of Delhi
  2. Vineet Kumar Kankerwal Department of Computer Science, Hansraj College, University of Delhi
  3. Priyam Gupta Department of Computer Science, Hansraj College, University of Delhi
  4. Vidhi Khanduja Department of Computer Science, Hansraj College, University of Delhi

Abstract

Dark patterns are deceptive design elements that influence user behavior online, frequently with unexpected results that go beyond personal experiences. These deceptive methods unintentionally encourage excessive consumption, which can seriously impede sustainability initiatives. This paper presents IntelliGuard WebScan, a system created specially to identify and combat these dishonest strategies. Employing painstaking examinations and assessment of heterogeneous datasets and rigorous experimentation with multiple algorithms, among them a support vector classifier (SVC), we were able to detect dark patterns with an astounding 93.22% accuracy. We propose a framework that uses an AI-powered browser plug-in with a user-friendly interface to find dark patterns in e-commerce websites. It scans the current webpage in real time and warns the user about the presence of dark patterns. Our methodology is designed to identify typical dark patterns that are present on e-commerce websites, such as social proof, obstruction, sneaking, scarcity, false urgency, forced action, and misdirection. Our ultimate objective is to provide users with the information and resources they need to navigate the online environment confidently and wisely, ultimately promoting a responsible online environment that gives priority to mindful consumption and is in line with long-term sustainability objectives.

Keywords

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