Opinion Dynamics in Social Networks: The Effect of Centralized Interaction Tuning on Emerging Behaviors

The algorithmic filtering of contents exchanged on digital social networks entails a centralized control of the intensity of users' interaction. The aim of this article is to investigate how this centralized action can affect the time evolution of users' opinions on a specific issue. To this purpose, this article proposes a stochastic multiagent model that incorporates a simplified description of the modulation of the interaction intensity exerted by the platform manager. Various transient and steady-state properties of the model are established. In particular, it is studied how emerging collective behaviors, e.g., consensus, polarization, and community cleavage, depend on the interaction intensity parameter. Notably, the nonmonotonic effects of such a parameter are observed for suitable distributions of influenceability across the community. By means of a newly introduced concept of individual stochastic social power, some insights are given on the role of the interaction intensity parameter in the conflict between opposite factions in simple scenarios. A major finding of this article is that an apparently neutral intervention, i.e., unbiased with respect to the conflicting opinions, can favor one faction just by tuning the interaction intensity.