Mouse tracking: measuring and predicting users' experience of web-based content

Previous studies have used mouse tracking as a tool to measure usability of webpages, user attention and search relevance. In this paper, we go beyond measurement of user behavior to prediction of the resulting user experience from mouse patterns alone. Specifically, we identify mouse markers that can predict user frustration and reading struggles at reasonably high accuracy. We believe that mouse-based prediction of user experience is an important advance, and could potentially offer a scalable way to infer user experience on the web. In addition, we demonstrate that mouse tracking could be used for applications such as evaluating content layout and content noticeability; we apply this in particular to advertisements. More generally, it could be used to infer user attention in complex webpages containing images, text and varied content, including how attention patterns vary with page layout and user distraction.