There is Something Beyond the Twitter Network

TL;DR

This paper critically examines how information spreads in social networks, introducing a new model that accounts for mass media influence and spontaneous information appearance, which better fits real-world data than existing models.

Contribution

It presents a novel rumor propagation model incorporating mass media effects and spontaneous information emergence, improving the accuracy of social network information flow modeling.

Findings

The new model fits real cascade size data better than previous models.

Mass media influence significantly impacts information spread patterns.

Spontaneous information appearance is a key factor in rumor propagation.

Abstract

How information spreads through a social network? Can we assume, that the information is spread only through a given social network graph? What is the correct way to compare the models of information flow? These are the basic questions we address in this work. We focus on meticulous comparison of various, well-known models of rumor propagation in the social network. We introduce the model incorporating mass media and effects of absent nodes. In this model the information appears spontaneously in the graph. Using the most conservative metric, we showed that the distribution of cascades sizes generated by this model fits the real data much better than the previously considered models.