Emergence of influential spreaders in modified rumor models

TL;DR

This paper introduces two new mechanisms in rumor spreading models to better capture the influence of certain nodes, aligning simulations more closely with real social network data.

Contribution

It proposes two novel mechanisms—spreader activity variability and interest levels—that improve the realism of rumor spreading models.

Findings

Models show higher agreement with real data

Influential spreaders emerge naturally in the modified models

Enhanced understanding of information diffusion mechanisms

Abstract

The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information. In this work, we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results. The first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor. In both cases, results from numerical simulations show a higher adhesion to real data than classical rumor spreading models. Our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models.