Misinformation spreading on activity-driven networks with heterogeneous spreading rates

Area of Science:

• Social Network Analysis
• Information Diffusion
• Computational Social Science

Background:

• Misinformation spread on social media is influenced by user forwarding behaviors.
• User relationships and link types create heterogeneity in information propagation.

Purpose of the Study:

• To introduce a novel ignorant-spreader-refractory (ISR) model for misinformation spread.
• To investigate the impact of heterogeneous spreading rates on activity-driven networks with diverse link types.

Main Methods:

• Developed a novel ISR spreading model incorporating heterogeneous spreading rates.
• Employed mean-field approach and Monte Carlo simulations for analysis.

Main Results:

• Heterogeneity in spreading rates has no effect on the outbreak threshold with uniform link distributions.
• Non-uniform link distributions show significant impact: normal distributions increase the threshold, while exponent distributions decrease it.
• The effect of heterogeneous spreading rates on final misinformation prevalence depends on link type distributions.

Conclusions:

• Heterogeneity of spreading rates is crucial for understanding misinformation dynamics.
• Network structure and link properties significantly modulate the impact of heterogeneous spreading rates.
• The ISR model provides a nuanced framework for studying information diffusion in complex social networks.