Context-Aware Detection of Deceptive Design Patterns in E-Commerce Websites Using Word Embedding Based Deep Learning Paradigms

Abstract

Deceptive designs (DDs) are a hidden technological tactic that manipulates the user's consumer behavior in a way that benefits website vendors without them knowing. Proper identification of deceptive designs is essential to prevent users from being misled by hidden tactics. To fulfill this requirement, this study assesses Word2Vec word embedding based deep learning models for text based deceptive design detection. Models trained consist of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid model (CNN + BiLSTM) that combines the two aforementioned models. These four key score indices of accuracy, precision, sensitivity, and F1-score are computed to compare the performance of each proclaimed model. When compared to the existing DD detection techniques, all three of these approaches attain state-of-the-art performance. The results of this evaluation illustrate that the hybrid model achieves the highest accuracy of 95% in capturing the nuanced text context of deceptive designs. Furthermore, even when other metrics are considered, the hybrid model performs more effectively. To guarantee the independence and security of user activities, intelligent deep learning paradigms are integrated to identify hidden deceptive activities automatically. This allows for the accurate detection and classification of deceptive designs in intricate e-commerce environments.