Collaborating in a purposive group, whether face-to-face or virtually, involves continuously expressing emotions and interpreting those of other group members. As such, understanding group affect is essential to comprehending how groups interact and succeed in collaborative efforts. In this study, we move beyond individual-level affect and investigate group-level affect - a collective phenomenon that reflects the shared mood or emotions among group members at a particular moment. As the first in the literature, we gather annotations for group-level affective expressions in purposive group interactions using a fine-grained temporal approach (15 s windows) that also captures the inherent dynamics of this collective construct. To this end, we extensively train annotators and develop an annotation procedure specifically tuned to capture the entire scope of the group interaction from one interaction moment to the next. In addition, we model the ebb and flow of group affect by accounting for the underlying convergence (driven by emotional contagion) and divergence (resulting from emotional reactivity) of affective expressions among group members. To capture these interpersonal dynamics, we employ two approaches: (i) extracting synchrony-based handcrafted features from both audio and visual modalities, and (ii) introducing a novel, data-driven graph neural network to model interpersonal dynamics among group members. Our results highlight the advantages of the graph network over the handcrafted features in modeling group affect, while also emphasizing the importance of temporal modeling and incorporating multimodal cues. Additionally, our analysis of affective convergence and divergence reveals that groups tend to diverge in their social signals during neutral collective affect, while exhibiting convergence during more emotionally intense moments. These insights are drawn from comparative results across both modeling techniques.

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.

In this paper we propose a novel method of estimating verbal expressions of task and social cohesion by quantifying the dynamic alignment of nonverbal behaviors in speech. As team cohesion has been linked to team effectiveness and productivity, automatically estimating team cohesion can be a useful tool for assessing meeting quality and broader team functioning. In total, more than 20 hours of business meetings (3-8 people) were recorded and annotated for behavioral indicators of group cohesion, distinguishing between social and task cohesion. We hypothesized that behaviors commonly referred to as mimicry can be indicative of verbal expressions of social and task cohesion. Where most prior work targets mimicry of dyads, we investigated the effectiveness of quantifying group-level phenomena. A dynamic approach was adopted in which both the cohesion expressions and the paralinguistic mimicry were quantified on small time windows. By extracting features solely related to the alignment of paralinguistic speech behavior, we found that 2-minute high and low social cohesive regions could be classified with a 0.71 Area under the ROC curve, performing on par with the state-of-the-art where turn-taking features were used. Estimating task cohesion was more challenging, obtaining an accuracy of 0.64 AUC, outperforming the state-of-the-art. Our results suggest that our proposed methodology is successful in quantifying group-level paralinguistic mimicry. As both the state-of-the-art turn-taking features and mimicry features performed worse on estimating task cohesion, we conclude that social cohesion is more openly expressed by nonverbal vocal behavior than task cohesion