Detecting Deceptive Dark Patterns in E-commerce Platforms

TL;DR

This paper presents a novel method combining web scraping and fine-tuned BERT models to detect deceptive dark patterns in e-commerce websites, enhancing detection accuracy and interpretability.

Contribution

It introduces an innovative approach that leverages BERT's language understanding and generation capabilities for identifying dark patterns, improving upon existing detection methods.

Findings

Effective detection of dark patterns using BERT-based models

Improved identification of outlier deceptive practices

Enhanced interpretability of dark pattern detection results

Abstract

Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers.