The advances in automatic facial expression recognition make it possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-theart facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers’ personality impressions using facial expressions and the Big-Five trait. Our results are promising, specially for the case of the Extraversion trait, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video