Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal be- havioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance signicantly.