Rumor propagation and supervision during confrontation: An importance-driven SIRQS network model

Area of Science:

• Social Network Analysis
• Information Science
• Computational Social Science

Background:

• Societal impact of rumor spreading is significant.
• Existing research often focuses on either rumor propagation or control, neglecting their dynamic interplay.
• A gap exists in understanding the confrontational relationship between rumor dynamics and control mechanisms.

Purpose of the Study:

• To propose a novel confrontation framework for rumor modeling.
• To extend the susceptible-infected-recovered-susceptible (SIRS) model to an ignorant-spreader-stifler-vigilant-ignorant (SIRQS) framework.
• To capture the confrontational behavior of individuals towards supervisors and integrate it into rumor dynamics.

Main Methods:

• Developed an ignorant-spreader-stifler-vigilant-ignorant (SIRQS) model incorporating a vigilant state and confrontation.
• Implemented a supervision mechanism using random walks guided by node propagation importance.
• Utilized a microscopic Markov chain approach to model heterogeneous node behavior and coupled propagation-supervision processes.

Main Results:

• Identified supervision subject, supervisor quantity preference, and confrontation mechanism as key factors in rumor control effectiveness.
• Demonstrated the substantial influence of these mechanisms in real-world spreading scenarios.
• Provided references for determining optimal supervisor numbers based on evaluation indicators.

Conclusions:

• The proposed SIRQS framework effectively models the complex relationship between rumor propagation and control.
• Supervision strategies, particularly the number of supervisors and confrontation, are critical for mitigating rumor spread.
• The study offers insights for designing effective rumor control interventions in online social networks.