Automated prediction of visual complexity of web pages: Tools and evaluations. Issue 145 (January 2021)

• Record Type: Journal Article
• Authors: Michailidou, Eleni; Eraslan, Sukru; Yesilada, Yeliz; Harper, Simon
• Is Part Of: International journal of human-computer studies. Issue 145(2021)
• Publication Date: 2021-01
• Subjects: Visual complexity -- Prediction -- Perception -- Automated tool; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2020.102523
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: A visual complexity prediction model proposed for web pages. The model discovers the visual complexity of web pages as perceived by users. The prediction model was implemented in a tool called ViCRAM. A confirmatory user study was conducted to validate the model and the tool. Abstract: Understanding visual complexity as it relates to websites has been an emergent area for many years. However, predicting the visual complexity of a website as perceived by users has been a real challenge. Perception is important because it influences user engagement, dictating if they will find it dull, engaging, or too complex. While others have suggested solutions to certain levels of success, here we propose a simple but accurate model that generates a Visual Complexity Score (VCS) based on common aspects of an HTML Document Object Model (DOM). We created our model based on a statistical analysis of 3300 ratings of 55 users on 30 web pages. We then implemented this prediction model in an open source Eclipse framework called ViCRAM that both predicts and visualises the complexity of web pages in the form of a pixelated heat map. Finally, we evaluated this model and the tool prediction with another user study of 6240 ratings of 104 users on 30 web pages. This study shows that our tool can predict the perceived complexity with a strong correlation to users' perceived complexity.