Twenty seconds of visual behaviour on social media gives insight into personality

Abstract

Abstract Eye tracking allows the researcher to capture individual differences in the expression of visual exploration behaviour, which in certain contexts has been found to reflect aspects of the user’s preferences and personality. In a novel approach, we recorded the eye movements of 180 participants whilst they browsed their Facebook News Feed and employed a machine learning approach to predict each of the self-reported Big Five personality traits from this viewing behaviour. We identify that specific visual behaviours are informative of an individual’s personality trait information, and can be used to psychologically profile social networking site users significantly better than chance after collecting only 20 seconds of viewing behaviour. We discuss potential applications for user engagement during human–computer interactions, and highlight potential privacy concerns.