ABSTRACT Dark patterns refer to certain elements of the user interface and user experience that are designed to deceive, manipulate, confuse, and pressure users of a particular platform or website into making decisions they wouldn’t have made knowingly. Many companies have begun implementing dark patterns on their websites, employing carefully crafted language and design elements to manipulate their users. Numerous studies have examined this subject and developed a classification system for these patterns. Additionally, governments worldwide have taken actions to restrict the use of these practices.
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Detecting dark patterns in shopping websites – a multi-faceted approach using Bidirectional Encoder Representations From Transformers (BERT)
The proposed work aims to enhance the accuracy for the detection of dark patterns using a natural language processing (NLP) model, i.e. BERT which results in accuracy 97% compared to classical models such as Random Forest and SVM having accuracy of 95.4% and 95.8% respectively.