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The Dynamical Model of Information Diffusion in Social Networks Considering the Effects of User Behavior

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  • Morteza Jouyban
  • Soodeh Hosseini
  • Mahdieh Khorashadizade

Abstract

User behavior directly influences the speed, spread, and content of information diffusion on social networks. Social networks can be regarded as social, complex, dynamic, and networked systems. Mathematical models are a powerful tool for better understanding the system behavior better, identifying key factors, predicting future behaviors, and developing management policies. This paper presents the susceptible‐exposed‐infected‐recovered‐susceptible with quitted and addicted (SEIRS‐QA) model, a dynamic framework that effectively captures user behavior in social networks over time. By incorporating addiction, popularity, and user awareness, this model offers insights into the dynamic nature of social network usage. The analytical model serves as the foundation for characterizing social networks, considering three key factors that shape their impact. The equilibrium points of the model are studied, and their stability is assessed using the Routh–Hurwitz criteria and comparison principle. The model’s dynamic behavior is influenced by the basic reproductive ratio, which is determined using the next‐generation matrix. To validate the theoretical results, numerical simulations are conducted, confirming the accuracy and reliability of the proposed approach. Through analytical and stability analyses, along with numerical simulations, this study provides a theoretical foundation to advance our understanding of information diffusion dynamics in social networks.

Suggested Citation

  • Morteza Jouyban & Soodeh Hosseini & Mahdieh Khorashadizade, 2025. "The Dynamical Model of Information Diffusion in Social Networks Considering the Effects of User Behavior," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jnddns:v:2025:y:2025:i:1:n:5876933
    DOI: 10.1155/ddns/5876933
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    References listed on IDEAS

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    1. Huang, He & Pan, Jialin & Chen, Yahong, 2024. "The competitive diffusion of knowledge and rumor in a multiplex network: A mathematical model," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    2. DeLegge, Anthony & Wangler, Hannah, 2017. "Is this the end for Facebook? A mathematical analysis," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 364-380.
    3. Tom Britton, 2020. "Epidemic models on social networks—With inference," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 222-241, August.
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