Perceived personality state estimation in dyadic and small group interaction with deep learning methods
This work addresses the challenge of understanding personality dynamics in collaborative settings, which is incremental but has practical implications for team analysis and social computing.
The paper tackled the problem of estimating perceived personality traits from thin slices of dyadic and small group interactions using a transformer-based model, finding that it performs similarly to human annotators and that group-level average perceived traits better predict group performance than self-reported traits.
Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We find that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-five personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we find that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.