A model of spreading of sudden events on social networks

TL;DR

This paper introduces a Susceptible-Accepted-Recovered model incorporating information sensitivity and social reinforcement to explain the diverse spreading speeds of events on social networks, supported by empirical data and theoretical analysis.

Contribution

It presents a novel SAR model with social reinforcement and information sensitivity, and an edge-based theory to explain rapid or slow information spreading patterns.

Findings

Model reproduces main spreading patterns of six events.

Spreading speed increases with information sensitivity or social reinforcement.

Final accepted size can shift from continuous to discontinuous transition.

Abstract

Information spreading has been studied for decades, but its underlying mechanism is still under debate, especially for those ones spreading extremely fast through Internet. By focusing on the information spreading data of six typical events on Sina Weibo, we surprisingly find that the spreading of modern information shows some new features, i.e. either extremely fast or slow, depending on the individual events. To understand its mechanism, we present a Susceptible-Accepted-Recovered (SAR) model with both information sensitivity and social reinforcement. Numerical simulations show that the model can reproduce the main spreading patterns of the six typical events. By this model we further reveal that the spreading can be speeded up by increasing either the strength of information sensitivity or social reinforcement. Depending on the transmission probability and information sensitivity, the final accepted size can change from continuous to discontinuous transition when the strength of the social reinforcement is large. Moreover, an edge-based compartmental theory is presented to explain the numerical results. These findings may be of significance on the control of information spreading in modern society.