An adaptive computational network model for multi-emotional social interaction

Abstract

The study reported in this paper investigates an adaptive temporal-causal network-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model’s potentials and limitations. The study advances the existing methodology of modeling the social contagion of multiple emotions in a context where also the social network evolves over time.