Detecting Dark Patterns in User Interfaces Using Logistic Regression and Bag-of-Words Representation

TL;DR

This paper introduces a machine learning approach using logistic regression and bag-of-words to detect dark patterns in user interfaces, aiming to enhance transparency and ethical design.

Contribution

It presents a novel methodology combining text feature extraction and logistic regression for effective dark pattern detection in user interfaces.

Findings

High accuracy and robustness in identifying dark patterns

Effective in diverse dataset conditions

Contributes to ethical UI design practices

Abstract

Dark patterns in user interfaces represent deceptive design practices intended to manipulate users' behavior, often leading to unintended consequences such as coerced purchases, involuntary data disclosures, or user frustration. Detecting and mitigating these dark patterns is crucial for promoting transparency, trust, and ethical design practices in digital environments. This paper proposes a novel approach for detecting dark patterns in user interfaces using logistic regression and bag-of-words representation. Our methodology involves collecting a diverse dataset of user interface text samples, preprocessing the data, extracting text features using the bag-of-words representation, training a logistic regression model, and evaluating its performance using various metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying instances of dark patterns, with high predictive performance and robustness to variations in dataset composition and model parameters. The insights gained from this study contribute to the growing body of knowledge on dark patterns detection and classification, offering practical implications for designers, developers, and policymakers in promoting ethical design practices and protecting user rights in digital environments.