Modeling Multiple Fields of Collective Emotions with Brownian Agent-Based Model

Understanding the emergence of collective emotions is critical to the analysis of online and offline societies. The agentbased simulation community has developed various social norm models to see the polarization of collective emotions. Yet, a few models have psychological background as fundamentals, as well as statistical validation, and this paper aims at resolving the two challenges. Particularly, this paper models agents as Brownian agents with different parameters of arousal, valence, and preference, which originates from the field of psychology. The Brownian agents enable the agents to become more heterogeneous, while they are simple enough to be understood and expanded. In the simulation, the agents influence and are influenced by multiple fields, or parts of community, of collective emotions. We designed two virtual experiments: one hypothetical setting to generate the emergence, and the other setting to validate the model with a real-world dataset. The first experiment identifies the scenario characteristics of extreme polarizations of collective emotions. The second experiment shows that the simple Brownian agent model is able to generate the real-world case with statistical significance.