Rumor propagation in the framework of evolutionary game analysis

Area of Science:

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

Background:

• Rumor propagation on social media is a growing concern.
• Information spread is influenced by content, user behavior, and media activity.
• Existing models often lack a comprehensive view of multi-actor interactions.

Purpose of the Study:

• To propose a novel rumor propagation interaction model.
• To integrate rumor-spreading dynamics with a three-party evolutionary game.
• To analyze interactions among netizens, media, and government on social platforms.

Main Methods:

• Developed a novel model combining rumor-spreading and evolutionary game theory.
• Utilized a physics-informed neural network for simulation.
• Integrated rumor model parameters into a tripartite evolutionary game matrix.
• Simulated real-world rumor spread data from the U.S. Twitter platform.

Main Results:

• Government control probability positively correlates with curbing rumor spread.
• Lower media rumor-spreading probability increases rumor debunking.
• Reduced government control costs lead to increased intervention, less media propagation, and more refutations.

Conclusions:

• The proposed model offers practical insights into rumor propagation dynamics.
• Government regulation and media behavior significantly impact online rumor diffusion.
• The model quantifies the interplay between regulatory efforts and platform actors.