Predicting whether users view dynamic content on the world wide web

Dynamic micro-content—interactive or updating widgets and features—is now widely used on the Web, but there is little understanding of how people allocate attention to it. In this article we present the results of an eye-tracking investigation examining how the nature of dynamic micro-content influences whether or not the user views it. We propose and validate the Dynamic Update Viewing-likelihood (DUV) model, a CHi-squared Automatic Interaction Detector (CHAID) model that predicts with around 80% accuracy whether users view dynamic updates as a function of how they are initiated, their size, and their duration. The model is constructed with data from live Web sites and does not rely on knowledge of the user's task to make its predictions, giving it a high level of external validity. We discuss one example of its application: informing how dynamic content should be presented in audio via assistive technology for people with visual impairments.