AdsDP: A Video Dataset for Recognizing and Examining Dark Patterns in iOS In-App Advertisements

Ads are widely deployed in mobile apps, significantly affecting user experience. In recent years, malicious or deceptive user interfaces (UI) designs, known as dark patterns, have increasingly been employed in in-app ads to manipulate users into unintended actions. While previous studies have identified a limited set of dark patterns in in-app ads and highlighted user concerns, a thorough understanding remains lacking, partly due to the absence of publicly available, contextual datasets on dark patterns in in-app ads. In this study, we systematically investigate dark patterns in iOS in-app ads, identifying 15 types, 11 of which were previously unreported. We also introduce AdsDP, an annotated video dataset documenting in-app ads appearing during normal usage of iOS apps, along with any dark patterns these ads may exhibit. AdsDP includes 718 videos totaling 60 hours and features 5,782 instances of ad-related dark patterns across 485 apps. Furthermore, we evaluate the performance of state-of-the-art dark pattern detection solutions using AdsDP, revealing a significant decline in performance on our novel dataset, underscoring the need for new detection methods. Finally, we demonstrate AdsDP's potential to enhance future detection efforts and increase user awareness of ad-related dark patterns. The AdsDP dataset is available at https://zenodo.org/records/15316373.