IntelliGaurd WebScan: Uncovering Dark Patterns on E-Commerce Websites

Year : 2024 | Volume : 11 | Issue : 03 | Page : 24 32
    By

    Yashasvi,

  • Vineet Kumar Kankerwal,

  • Priyam Gupta,

  • Vidhi Khanduja,

  1. Student, Department of Computer Science, Hansraj College, University of Delhi, Delhi, India
  2. Student, Department of Computer Science, Hansraj College, University of Delhi, Delhi, India
  3. Student, Department of Computer Science, Hansraj College, University of Delhi, Delhi, India
  4. Assistant Professor, Department of Computer Science, Hansraj College, University of Delhi, Delhi, India

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: Dark patterns, support vector classifier, UI/UX, IntelliGuard WebScan, e-commerce websites

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article:
Yashasvi, Vineet Kumar Kankerwal, Priyam Gupta, Vidhi Khanduja. IntelliGaurd WebScan: Uncovering Dark Patterns on E-Commerce Websites. E-Commerce for Future & Trends. 2024; 11(03):24-32.
How to cite this URL:
Yashasvi, Vineet Kumar Kankerwal, Priyam Gupta, Vidhi Khanduja. IntelliGaurd WebScan: Uncovering Dark Patterns on E-Commerce Websites. E-Commerce for Future & Trends. 2024; 11(03):24-32. Available from: https://journals.stmjournals.com/ecft/article=2024/view=169266


References

  1. Koh WC, Seah YZ. Unintended consumption: The effects of four e-commerce dark patterns. Cleaner Responsible Consum. 2023;11:100145. DOI: 10.1016/j.clrc.2023.100145.
  2. Di Geronimo L, Braz L, Fregnan E, Palomba F, Bacchelli A. UI dark patterns and where to find them: A study on mobile applications and user perception. CHI Conf Hum Factors Comput Syst. 2020;1–14. DOI: 10.1145/3313831.3376600.
  3. Mathur A, Acar G, Friedman MJ, Lucherini E, Mayer J, Chetty M, et al. Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proc ACM Hum-Comput Interact. 2019;3:1–32. DOI: 10.1145/3359183.
  4. Feng JY, Yuki T, Matsumoto N, Fukushima F, Kido H, Yamana H. Dark patterns in e-commerce: A dataset and its baseline evaluations. IEEE Int Conf Big Data. 2022;3015–22.
  5. Chen J, Sun J, Feng S, Xing Z, Lu Q, Xu X, Chen C. Unveiling the tricks: Automated detection of dark patterns in mobile applications. 36th Annu ACM Symp User Interface Softw Technol. 2023;1–20. DOI: 10.1145/3586183.3606783.
  6. Bankel M. Exploring the use of dark patterns in the donation processes of nonprofit eCommerce. In: Mejtoft T, Söderström U, Norberg O, Freidovich L, editors. Proceedings of the 21st Student Conference in Interaction Technology and Design; 2021 Jun; Umeå, Sweden. Umeå: Umeå University; 2021. p. 65–9.
  7. Gray CM, Kou Y, Battles B, Hoggatt J, Toombs AL. The dark (patterns) side of UX design. CHI Conf Hum Factors Comput Syst. 2018;1–14. DOI: 10.1145/3173574.3174108.
  8. Nevala E. Dark patterns and their use in e-commerce [Bachelor’s thesis]. Jyväskylä: University of Jyväskylä; 2020.
  9. Kodandaram SR, Sunkara M, Jayarathna S, Ashok V. Detecting deceptive dark-pattern web advertisements for blind screen-reader users. J Imaging. 2023;9:239. DOI: 10.3390/jimagingPubMed: 37998086.
  10. Mansur SMH, Salma S, Awofisayo D, Moran K. AidUI: Toward automated recognition of dark patterns in user interfaces. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), Melbourne, Australia. 2023. pp. 1958–70. doi: 10.1109/ICSE48619.2023.
  11. (2017). Removing stop words with NLTK in Python. [online] Available from: https://www.geeksforgeeks.org/removing-stop-words-nltk-python/.
  12. Awan AA. (2023). What is tokenization? [online] Datacamp.com. Available from: https://www.datacamp.com/blog/what-is-tokenization.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 18/06/2024
Accepted 08/07/2024
Published 27/08/2024
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