Rumor spread in social network: Difference between revisions

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== HISB Model ==

In recent years, the rampant spread of misinformation and rumors on online social networks (OSNs) has raised significant concerns within the scientific and technological communities. OSNs are powerful tools for disseminating information, but they can also accelerate the propagation of false or misleading content, which may have serious societal consequences. Consequently, the study of rumor dynamics and the development of effective mitigation strategies have become crucial areas of research.

The [[Independent Cascade]] (IC) and [[Linear Threshold]] (LT) models are well-established information [[diffusion models]] originally proposed by Kempe et al. However, several studies have raised concerns about their ability to reflect realistic rumor propagation dynamics in [[online social networks]] (OSNs). In response, alternative models have been developed, including the Energy model, the Forest Fire model, and the HISBmodel. Among these, the HISBmodel is notable for incorporating both individual and social behaviors, enabling it to more accurately replicate real-world rumor dynamics. It is also adaptable to multiplex and multilayer OSNs.

Several models have been proposed to analyze and control rumor spread in OSNs. Among them, the HISB model (Human Individual and Social Behavior model) stands out as a comprehensive approach that incorporates both individual cognitive factors and social influence mechanisms. This model was first introduced in <ref>A.I.E. Hosni, K. Li, S. Ahmed, HISBmodel : A Rumor Diffusion Model Based on Human Individual and Social Behaviors in Online Social Networks, in: Springer, 2018.</ref>, where the authors proposed a rumor diffusion framework based on human behaviors in online social environments. It was later refined and applied in a more practical context for rumor mitigation in <ref>A.I.E. Hosni, K. Li, S. Ahmed, Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors, in: Elsivier, 2019 Information science .</ref>.

Subsequent enhancements were made in <ref>A.I.E. Hosni, K. Li, Minimizing the influence of rumors during breaking news events in online social networks, in: Elsivier, 2019 knowledge based systems .</ref>, where the model was adapted for breaking news scenarios, addressing the challenges of real-time rumor control. Most recently, a hybrid approach incorporating the HISB model was proposed by <ref>Hosni, A.I.E., Baira, I., Merini, H. et al. Efficient hybrid rumor mitigation in dynamic and multilayer online social networks. Soc. Netw. Anal. Min. 14, 224 (2024). https://doi.org/10.1007/s13278-024-01381-z </ref>.

These successive contributions illustrate the robustness and adaptability of the HISB framework in tackling the evolving nature of misinformation in OSNs. This article provides a comprehensive overview of rumor propagation models, with a focus on those incorporating behavioral dynamics, and discusses state-of-the-art strategies for minimizing rumor influence.

This article adopts the HISBmodel for simulating rumor diffusion. Below, we provide an overview of its key components.