Social signal processing develops automated approaches to detect, analyze, and synthesize social signals in human–human as well as human–machine interactions by means of machine learning and sensor data processing. Most works analyze individual or dyadic behavior, while the analysis of group or team interactions remains limited. We present a case study of an interdisciplinary work process for social signal processing that can develop automatized measures of complex team interaction dynamics, using team task and social cohesion as an example. In a field sample of 25 real project team meetings, we obtained sensor data from cameras, microphones, and a smart ID badge measuring acceleration. We demonstrate how fine-grained behavioral expressions of task and social cohesion in team meetings can be extracted and processed from sensor data by capturing dyadic coordination patterns that are then aggregated to the team level. The extracted patterns act as proxies for behavioral synchrony and mimicry of speech and body behavior which map onto verbal expressions of task and social cohesion in the observed team meetings. We reflect on opportunities for future interdisciplinary or collaboration that can move beyond a simple producer–consumer model.

There has been gathering momentum over the last 10 years in the study of group behavior in multimodal multiparty interactions. While many works in the computer science community focus on the analysis of individual or dyadic interactions, we believe that the study of groups adds an additional layer of complexity with respect to how humans cooperate and what outcomes can be achieved in these settings. Moreover, the development of technologies that can help to interpret and enhance group behaviours dynamically is still an emerging field. Social theories that accompany the study of groups dynamics are in their infancy and there is a need for more interdisciplinary dialogue between computer scientists and social scientists on this topic. This workshop has been organised to facilitate those discussions and strengthen the bonds between these overlapping research communities

Over the last decade, the study of group behavior for multimodal interaction technologies has increased. However, we believe that despite its potential benefits on society, there could be more activity in this area. The aim of this workshop is create a forum for more interdisciplinary dialogue on this topic to enable the acceleration of growth. The workshop has been very successful in attracting submissions addressing important facets in the context of technologies for analyzing and aiding groups. This paper provides a summary of the activities of the workshop and the accepted papers.

This special issue on advancing interdisciplinary collaboration between computer scientists and social scientists documents the joint results of the international Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” which took place in Leiden, The Netherlands, July 2016. An equal number of scholars from social and computer science participated in the workshop and contributed to the papers included in this special issue. In this introduction, we first identify interaction dynamics as the core of group and team models and review how scholars in social and computer science have typically approached behavioral interactions in groups and teams. Next, we identify key challenges for interdisciplinary collaboration between social and computer scientists, and we provide an overview of the different articles in this special issue aimed at addressing these challenges.