Group emotional entropy: a perspective on the pathways of collective intelligence generation

This study investigates the impact of group emotion regulation on collective intelligence following emergencies. While existing research often employs Shannon’s entropy framework to model emotional entropy, it tends to overlook temporal dynamics and the influence of group size. In response, we propose a novel emotional entropy model that incorporates both spatial and temporal dimensions of group emotions, aiming to reveal the nature of group emotional entropy and its role in collective intelligence. Based on group dynamics and emotion dynamics, we model the evolution of group size and emotional information, thereby developing an emotional entropy model that reflects the progression of group emotions. Theoretical analysis identifies the conditions for maximum and minimum entropy. Empirical validation using sentiment data from four emergency events confirms the model’s accuracy, with close alignment between observed data and model predictions. We introduce a data-driven method for calculating group emotional entropy and the concept of informational negentropy, which elucidates the mechanisms of collective intelligence formation through integration and differentiation. By analyzing entropy changes and energy dynamics, we identify two key pathways to collective intelligence. This study provides a new theoretical framework for understanding collective intelligence and offers insights for optimizing group emotion regulation.