# Dark patterns in e-commerce: a dataset and its baseline evaluations

arXiv: https://arxiv.org/abs/2211.06543 · HTML: https://mlsanity.com/paper/2211.06543/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites](https://mlsanity.com/paper/1907.07032/) (2019)
- [What Makes a Dark Pattern... Dark? Design Attributes, Normative Considerations, and Measurement Methods](https://mlsanity.com/paper/2101.04843/) (2021)
- [An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest](https://mlsanity.com/paper/1803.08488/) (2018)
- [Circumvention by design -- dark patterns in cookie consents for online news outlets](https://mlsanity.com/paper/2006.13985/) (2020)
- [Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest](https://mlsanity.com/paper/1809.00620/) (2018)
- [Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way](https://mlsanity.com/paper/2204.11836/) (2022)

## Built on by (most cited first, 3 of 3)

- [Why is the User Interface a Dark Pattern? : Explainable Auto-Detection and its Analysis](https://mlsanity.com/paper/2401.04119/) (2023)
- [Detecting Dark Patterns in User Interfaces Using Logistic Regression and Bag-of-Words Representation](https://mlsanity.com/paper/2412.14187/) (2024)
- [Building UI/UX Dataset for Dark Pattern Detection and YOLOv12x-based Real-Time Object Recognition Detection System](https://mlsanity.com/paper/2512.18269/) (2025)

## Abstract (arXiv)

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.'s study in 2019, which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.'s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.