Micro-level dynamics of the online information propagation: A user behavior model based on noisy spiking neurons

• We introduce a micro-level model of the online information propagation patterns.
• The online users’ behavior is modeled as analogous to the dynamics of noisy spiking neurons.
• We incorporate internal and external, deterministic and stochastic sources of influence.
• The model qualitatively and quantitatively reproduces real information propagation patterns.
• The proposed method introduces a new framework for social simulations.

We develop and validate a model of the micro-level dynamics underlying the formation of macro-level information propagation patterns in online social networks. In particular, we address the dynamics at the level of the mechanism regulating a user’s participation in an online information propagation process. We demonstrate that this mechanism can be realistically described by the dynamics of noisy spiking neurons driven by endogenous and exogenous, deterministic and stochastic stimuli representing the influence modulating one’s intention to be an information spreader. Depending on the dynamically changing influence characteristics, time-varying propagation patterns emerge reflecting the temporal structure, strength, and signal-to-noise ratio characteristics of the stimulation driving the online users’ information sharing activity. The proposed model constitutes an overarching, novel, and flexible approach to the modeling of the micro-level mechanisms whereby information propagates in online social networks. As such, it can be used for a comprehensive understanding of the online transmission of information, a process integral to the sociocultural evolution of modern societies. The proposed model is highly adaptable and suitable for the study of the propagation patterns of behavior, opinions, and innovations among others.