Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

Area of Science:

• Social network analysis
• Information diffusion modeling
• Computational social science

Background:

• Rumor spreading poses significant societal risks.
• Understanding rumor dynamics is crucial for control and mitigation.
• Individual behavior and information characteristics influence rumor propagation.

Purpose of the Study:

• To propose a novel mathematical model (SEIsIrR) for rumor spreading.
• To investigate the impact of personality types (radical vs. steady), rumor credibility, and life relevance on rumor dynamics.
• To analyze rumor spreading in both homogeneous and heterogeneous networks.

Main Methods:

• Development of differential equations to model rumor dynamics.
• Utilizing Jacobian and next-generation matrices to determine the spreading threshold.
• Verification of the model using a real-world Twitter rumor dataset.
• Numerical simulations on Watts-Strogatz (WS) and Barabasi-Albert (BA) networks.

Main Results:

• The SEIsIrR model effectively captures rumor spreading dynamics.
• Personality, credibility, and relevance significantly affect the speed and range of rumor diffusion.
• Spreading thresholds differ between homogeneous and heterogeneous networks.
• Simulation results align with theoretical predictions for WS and BA networks.

Conclusions:

• The SEIsIrR model provides a robust framework for studying rumor propagation.
• Individual psychological factors and information attributes are key determinants of rumor spread.
• Network structure (homogeneous vs. heterogeneous) influences the overall spreading dynamics.