The Attentive Cursor Dataset
Luis A. Leiva, Ioannis Arapakis · Frontiers in Human Neuroscience · 2020
We introduce a large-scale dataset of mouse cursor movements that can be used to predict user attention, infer demographics information, and analyze fine-grained movements.Attention is a finite resource, so people spend their time on things they find valuable, especially when browsing online.Objective measurements of attentional processes are increasingly sought after by researchers, advertisers, and other key stakeholders from both academia and industry.With every click, digital footprints are created and logged, providing a detailed record of a person's online activity.However, click data provide an incomplete picture of user interaction, as they inform mainly about a users' end choice.A user click is often preceded by several valuable interactions, such as scrolling, hovers, aimed movements, etc. and thus having access to this kind of data can lead to an overall better understanding of the user's cognitive processes.For example, previous work has evidenced that when the mouse cursor is motionless, the user is processing information (Hauger et al., 2011;Huang et al., 2011;Diriye et al., 2012;Boi et al., 2016), i.e., essentially "users first focus and then execute actions" (Martín-Albo et al., 2016).We have collected mouse cursor tracking logs from near 3K subjects performing a transactional search task that together account for roughly 2 h worth of interaction data.Our dataset has associated attention labels and five demographics attributes that may help researchers to conduct several analysis, like the ones we discuss later in this section.Research in mouse cursor tracking has a long track record.Chen et al. (2001) were among the first ones to note a relationship between gaze position and cursor position during web browsing.Mueller and Lockerd (2001) investigated the use of mouse tracking to create compelling visualizations and model the users' interests.It has been argued that mouse movements can reveal subtle patterns like reading (Hauger et al., 2011) or hesitation (Martín-Albo et al., 2016), and can help the user regain context after an interruption (Leiva, 2011a).Others have also noted the utility of mouse cursor analysis as a low-cost and scalable proxy of eye tracking (Huang et al.,