A dynamics-based model of multi-state rumor propagation with triple identities

With the wide application of social networks, rumor spreading has emerged as a critical issue affecting social stability and public order. In order to deeply study the dynamic characteristics of rumor propagation, this paper proposes a dynamics-based multi-state rumor propagation model with triple identities. The model combines individual identities (rumor publishers, rumor controllers, and ordinary participants) and multiple dynamic states of individuals (e.g., susceptible, latent, and ignorant spreaders, etc.) in social networks, and describes the rumor spreading process in social networks by differential equations. With the model, rumor publishers influence ordinary participants by spreading rumors, while rumor controllers prevent the further spread of rumors through immune mechanisms. Meanwhile, the propagation status of individuals changes over time, including the transition from susceptible to ignorant propagator and malicious propagator statuses. By simulation experiments using real social network data, the effects of different network structures, propagation parameters and control strategies on rumor spreading are evaluated. The results show that individual identity and network structure have a significant impact on rumor spreading, and the effective intervention of rumor controllers can significantly slow down the speed and scope of rumor spreading. This paper provides a theoretical basis for comprehension of the rumor spreading mechanism and establishment of rumor prevention and control strategies, and offers new ideas for public crisis management based on social networks.

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Publication Details

Journal
Scientific Reports
Published
2025-11-12
DOI
https://doi.org/10.1038/s41598-025-22166-0
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A dynamics-based model of multi-state rumor propagation with triple identities

Tao Li, Zhiqiang Su, Y. H. Zheng, Ailian Wang et al.
Scientific Reports
Complex Network Analysis Techniques
article

A dynamics-based model of multi-state rumor propagation with triple identities

Tao Li, Zhiqiang Su, Y. H. Zheng, Ailian Wang, X. Gao
article en

Abstract

With the wide application of social networks, rumor spreading has emerged as a critical issue affecting social stability and public order. In order to deeply study the dynamic characteristics of rumor propagation, this paper proposes a dynamics-based multi-state rumor propagation model with triple identities. The model combines individual identities (rumor publishers, rumor controllers, and ordinary participants) and multiple dynamic states of individuals (e.g., susceptible, latent, and ignorant spreaders, etc.) in social networks, and describes the rumor spreading process in social networks by differential equations. With the model, rumor publishers influence ordinary participants by spreading rumors, while rumor controllers prevent the further spread of rumors through immune mechanisms. Meanwhile, the propagation status of individuals changes over time, including the transition from susceptible to ignorant propagator and malicious propagator statuses. By simulation experiments using real social network data, the effects of different network structures, propagation parameters and control strategies on rumor spreading are evaluated. The results show that individual identity and network structure have a significant impact on rumor spreading, and the effective intervention of rumor controllers can significantly slow down the speed and scope of rumor spreading. This paper provides a theoretical basis for comprehension of the rumor spreading mechanism and establishment of rumor prevention and control strategies, and offers new ideas for public crisis management based on social networks.

Scientific ReportsVol. 15(1)
Taiyuan University of Science and Technology (CN), Taiyuan University of Technology (CN)
Ministry of Education of the People's Republic of China
Openalex Percentile: Top 73%
Complex Network Analysis Techniques
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