Misinformation spreading on correlated multiplex networks

TL;DR

This paper develops a theoretical framework to understand how misinformation spreads across correlated multiplex online social networks, revealing how network structure influences outbreak thresholds and sizes.

Contribution

It introduces a multiplex network-based misinformation spreading model and a heterogeneous edge-based compartmental theory to analyze dynamics and thresholds.

Findings

Outbreak size grows continuously with transmission probability beyond a threshold.

Higher average degrees and positive inter-layer correlation lower the outbreak threshold.

Degree heterogeneity and inter-layer correlation significantly affect outbreak size and speed.

Abstract

The numerous expanding online social networks offer fast channels for misinformation spreading, which could have a serious impact on socioeconomic systems. Researchers across multiple areas have paid attention to this issue with a view of addressing it. However, no systematical theoretical study has been performed to date on observing misinformation spreading on correlated multiplex networks. In this study, we propose a multiplex network-based misinformation spreading model, considering the fact that each individual can obtain misinformation from multiple platforms. Subsequently, we develop a heterogeneous edge-base compartmental theory to comprehend the spreading dynamics of our proposed model. In addition, we establish an analytical method based on stability analysis to obtain the misinformation outbreak threshold. On the basis of these theories, we finally analyze the influence of different dynamical and structural parameters on the misinformation spreading dynamics. Results show that the misinformation outbreak size $R(\infty)$ grows continuously with the effective transmission probability $\beta$ once $\beta$ exceeds a certain value, that is, the outbreak threshold $\beta_c$. A large average degrees, strong degree heterogeneity, or positive inter-layer correlation will reduce $\beta_c$, accelerating the outbreak of misinformation. Besides, increasing the degree heterogeneity or a more positive inter-layer correlation will both enlarge (reduce) $R(\infty)$ for small (large) values of $\beta$. Our systematic theoretical analysis results agree well with the numerical simulation results. Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks, and thereby pave the way to address this serious issue.