Rumor diffusion in an interests-based dynamic social network.

Area of Science:

• Social Network Analysis
• Information Diffusion
• Computational Social Science

Background:

• Understanding rumor diffusion is crucial in social networks.
• Existing models often lack dynamic, interest-based network structures.
• The evolution of information through word-of-mouth is a key factor.

Purpose of the Study:

• To propose a dynamic, interest-based social friend network model.
• To develop a rumor diffusion model for analyzing network characteristics.
• To investigate how network structure influences rumor spread.

Main Methods:

• Development of a dynamic friend network model with clustering and community features.
• Implementation of a rumor diffusion model based on the zombie-city model.
• Conducting simulation experiments on social friend networks.

Main Results:

• Positive information can transform into rumors via word-of-mouth diffusion.
• Random networks with lower clustering coefficients promote rumor diffusion more than dynamic networks.
• Rumors spread wider in networks with smaller global clustering coefficients.
• Networks with smaller clustering coefficients exhibit higher efficiency.

Conclusions:

• Network clustering significantly impacts rumor diffusion dynamics.
• Dynamic, interest-based networks and random networks show different diffusion patterns.
• Optimizing network structures for information flow requires considering clustering properties.