We propose an approach to identify users' exposure to fake news from users' gaze and mouse movement behavior. Our approach is meant as an enabler for interventions that make users aware of engaging with fake news while not being consciously aware of this. Our work is motivated by the rapid spread of fake news on the web (in particular, social media) and the difficulty and effort required to identify fake content, either technically or by means of a human fact checker. To this end, we set out with conducting a remote online study ($N=54$) in which participants were exposed to real and fake social media posts while their mouse and gaze movements were recorded. We identify the most predictive gaze and mouse movement features and show that fake news can be predicted with 68.4% accuracy from users' gaze and mouse movement behavior. Our work is complemented by discussing the implications of using behavioral features for mitigating the spread of fake news on social media.    «

We propose an approach to identify users' exposure to fake news from users' gaze and mouse movement behavior. Our approach is meant as an enabler for interventions that make users aware of engaging with fake news while not being consciously aware of this. Our work is motivated by the rapid spread of fake news on the web (in particular, social media) and the difficulty and effort required to identify fake content, either technically or by means of a human fact checker. To this end, we set out wit...    »