Understanding the Sneaky Patterns of Pop-up Windows in the Mobile Ecosystem
Dongpeng Wu, Yuhong Nan, Shaojiang Wang, Jiawei Wang, Luwa Li, Xueqiang Wang
TL;DR
This work investigates Sneaky patterns in mobile pop-up windows (PoWs) and their impact on user experience, introducing Poker, an automated app exploration framework. Poker combines a YOLO-based PoW Identifier with opacity analysis, a CV-driven PoW Dismissal module, and Adaptive Depth-First Search with state abstraction to robustly collect PoWs across apps. In evaluations over a dataset of PoWs and fifty apps, Poker achieves high identification accuracy and dismisses the majority of PoWs with minimal user interaction, outperforming existing tools in PoW collection. Cross-region analysis of the top one hundred apps in China and the U.S. reveals more PoWs and Sneaky instances in Chinese apps, with promotional patterns particularly prevalent in shopping and video apps, highlighting ethical concerns and the need for better design practices.
Abstract
In mobile applications, Pop-up window (PoW) plays a crucial role in improving user experience, guiding user actions, and delivering key information. Unfortunately, the excessive use of PoWs severely degrades the user experience. These PoWs often sneakily mislead users in their choices, employing tactics that subtly manipulate decision-making processes. In this paper, we provide the first in-depth study on the Sneaky patterns in the mobile ecosystem. Our research first highlights five distinct Sneaky patterns that compromise user experience, including text mislead, UI mislead, forced action, out of context and privacy-intrusive by default. To further evaluate the impact of such Sneaky patterns at large, we developed an automated analysis pipeline called Poker, to tackle the challenges of identifying, dismissing, and collecting diverse PoWs in real-world apps. Evaluation results showed that Poker achieves high precision and recall in detecting PoWs, efficiently dismissed over 88% of PoWs with minimal user interaction, with good robustness and reliability in comprehensive app exploration. Further, our systematic analysis over the top 100 popular apps in China and U.S. revealing that both regions displayed significant ratios of Sneaky patterns, particularly in promotional contexts, with high occurrences in categories such as shopping and video apps. The findings highlight the strategic deployment of Sneaky tactics that compromise user trust and ethical app design.