# Detecting Deceptive Dark Patterns in E-commerce Platforms

arXiv: https://arxiv.org/abs/2406.01608 · HTML: https://mlsanity.com/paper/2406.01608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 27 May 2024.

## Built on by (most cited first, 1 of 1)

- [Detecting Dark Patterns in User Interfaces Using Logistic Regression and Bag-of-Words Representation](https://mlsanity.com/paper/2412.14187/) (2024)

## Abstract (arXiv)

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.