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Percept."],"published-print":{"date-parts":[[2016,5,28]]},"abstract":"<jats:p>Eye tracking is one of the most exploited techniques in literature for finding usability problems in web-based user interfaces (UIs). However, it is usually employed in a laboratory setting, considering that an eye-tracker is not commonly used in web browsing. In contrast, web application providers usually exploit remote techniques for large-scale user studies (e.g. A\/B testing), tracking low-level interactions such as mouse clicks and movements. In this article, we discuss a method for predicting whether the user is looking at the content pointed by the cursor, exploiting the mouse movement data and a segmentation of the contents in a web page. We propose an automatic method for segmenting content groups inside a web page that, applying both image and code analysis techniques, identifies the user-perceived group of contents with a mean pixel-based error around the 20%. 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