Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{176834,
        author = {Satyajit S K and Sabari M and Manimeghala P},
        title = {Dark Pattern Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7694-7697},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176834},
        abstract = {This project introduces a robust system for the automated detection of dark patterns on websites, aiming to enhance user protection and transparency in online interactions. Leveraging a Naive Bayes classifier trained on dark pattern categories such as Bait and Switch, Forced Continuity, Price Comparison Prevention, Hidden Costs, and Sneaking, the model achieves effective identification of deceptive design elements. The preprocessing of textual data involves employing the TFIDF vectorizer for feature extraction, optimizing the classifier's performance. Web scraping is facilitated through cloud scraping techniques and Beautiful Soup, enabling the extraction of relevant data for classification. The resulting model file is applied to classify scraped data, empowering users to make informed decisions while navigating online interfaces. This innovative approach addresses the ethical concerns associated with dark patterns and contributes to a safer and more transparent online environment.},
        keywords = {Dark pattern detection, Naive Bayes classifier, TFIDF vectorizer, Web scraping, Cloud scraper, User protection, Transparency, Deceptive design, Online ethics.},
        month = {May},
        }