Dynamics of Collective Group Affect

Abstract

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.