# Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning

arXiv: https://arxiv.org/abs/1902.11247 · HTML: https://mlsanity.com/paper/1902.11247/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 18 of 18)

- [Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning](https://mlsanity.com/paper/2108.03353/) (2021)
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- [ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations](https://mlsanity.com/paper/2310.04869/) (2023)
- [UIClip: A Data-driven Model for Assessing User Interface Design](https://mlsanity.com/paper/2404.12500/) (2024)
- [Understanding Mobile GUI: from Pixel-Words to Screen-Sentences](https://mlsanity.com/paper/2105.11941/) (2021)
- [Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis](https://mlsanity.com/paper/2204.02448/) (2022)
- [Multimodal Icon Annotation For Mobile Applications](https://mlsanity.com/paper/2107.04452/) (2021)
- [DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation](https://mlsanity.com/paper/2410.00201/) (2024)
- [Tell Me What's Next: Textual Foresight for Generic UI Representations](https://mlsanity.com/paper/2406.07822/) (2024)
- [Explorer: Robust Collection of Interactable GUI Elements](https://mlsanity.com/paper/2504.09352/) (2025)
- [The role of large language models in UI/UX design: A systematic literature review](https://mlsanity.com/paper/2507.04469/) (2025)
- [Synthetic Cognitive Walkthrough: Aligning Large Language Model Performance with Human Cognitive Walkthrough](https://mlsanity.com/paper/2512.03568/) (2025)
- [GhostUI: Unveiling Hidden Interactions in Mobile UI](https://mlsanity.com/paper/2601.19258/) (2026)
- [Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze](https://mlsanity.com/paper/2206.09480/) (2022)
- [AI in HCI Design and User Experience](https://mlsanity.com/paper/2301.00987/) (2023)
- [MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs](https://mlsanity.com/paper/2403.13690/) (2024)

## Abstract (arXiv)

Tapping is an immensely important gesture in mobile touchscreen interfaces, yet people still frequently are required to learn which elements are tappable through trial and error. Predicting human behavior for this everyday gesture can help mobile app designers understand an important aspect of the usability of their apps without having to run a user study. In this paper, we present an approach for modeling tappability of mobile interfaces at scale. We conducted large-scale data collection of interface tappability over a rich set of mobile apps using crowdsourcing and computationally investigated a variety of signifiers that people use to distinguish tappable versus not-tappable elements. Based on the dataset, we developed and trained a deep neural network that predicts how likely a user will perceive an interface element as tappable versus not tappable. Using the trained tappability model, we developed TapShoe, a tool that automatically diagnoses mismatches between the tappability of each element as perceived by a human user---predicted by our model, and the intended or actual tappable state of the element specified by the developer or designer. Our model achieved reasonable accuracy: mean precision 90.2% and recall 87.0%, in matching human perception on identifying tappable UI elements. The tappability model and TapShoe were well received by designers via an informal evaluation with 7 professional interaction designers.