In this rapidly digitizing world, it is becoming ever more important to understand people’s online behaviors in both commercial and research settings. A cost-effective way to gain a deeper understanding of these behaviors is to examine mouse movement patterns. This research explores the feasibility of inferring personality traits from these patterns. Linear regression, correlation analyses, and PLS regression were performed to examine the relationships between cursor movement features such as pauses, fixations, and cursor speed, and two types of internal characteristics — general attentiveness and personality traits. Significant relationships were found between these features: mouse movement features such as clicks and fixations are significantly correlated with attentiveness, whereas attentiveness is significantly correlated with all five dimensions of the Big Five personality traits. Our results validate the feasibility of using mouse movement data to infer internal traits. These findings have possible applications in two realms: 1) the method presented here can be applied in research to filter inattentive participants, 2) commercial user experience research can apply the same method to gain insights on users’ personality to better cater to their demands .