Modeling, analysis and synthesis of behaviour are the subject of major efforts in computing science, especially when it comes to technologies that make sense of humanhuman and human-machine interactions. This article outlines some of the most important issues that still need to be addressed to ensure substantial progress in the field, namely 1) development and adoption of virtuous data collection and sharing practices, 2) shift of the focus of interest from individuals to dyads and groups, 3) endowment of artificial agents with internal representations of users and context, 4) modeling of cognitive and semantic processes underlying social behaviour, and 5) identification of application domains and strategies for moving from laboratory to the realworld products.

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This paper is concerned with investigating conversational (verbal and non verbal) features related to conversational dominance and discovering evident relations between these features, personality traits and perceived dominance scores. Ordinal regression models were applied to find associations between dominance scores, both as a predicted value and as a predictor, and personality traits and verbal features. Results report the association of high dominance scores to the large number of words a speaker utters per minute, as well as to high extraversion and high openness traits. Low dominance scores have been associated with high agreeableness.

This paper describes a recently created multimodal corpus that has been designed to address multiparty interaction modelling, specifically collaborative aspects in task-based group interactions. A set of human-human interactions was collected with HD cameras, microphones and a Kinect sensor. The scenario involves 2 participants playing a game instructed and guided by a facilitator. Additionally to the recordings, survey material was collected, including personality tests of the participants and experience assessment questionnaires. The corpus will be exploited for modelling behavioral aspects in collaborative group interaction by taking into account the speakers' multimodal signals and psychological variables.