A multiple information model incorporating limited attention and information environment

Area of Science:

• Information Science
• Network Science
• Computational Social Science

Background:

• Mobile social networks are expanding rapidly, increasing information spread speed and scale.
• Existing communication networks struggle to handle information surges, especially during critical events.
• Understanding the dynamics of viral information and hot news is crucial for online platforms.

Purpose of the Study:

• To propose a novel information propagation model that accounts for local information environment and limited human attention.
• To analyze the factors contributing to the prolonged existence of "hot news" compared to other online information.
• To provide insights for information platforms to effectively filter, push, and maintain trending news.

Main Methods:

• Developed a multiple information propagation model based on the Susceptible Infected Recovered (SIR) model.
• Introduced "heat rate" and "popular rate" to quantify local influence and user attention.
• Conducted theoretical analysis and numerical simulations on uniform and scale-free networks.

Main Results:

• Early popular information gains more attention and spreads more easily than later information.
• Limited attention significantly influences information spread dynamics, especially when multiple pieces of information compete.
• Early popular information demonstrates greater resilience to increased acceptance thresholds and sensitivity to decreased rejection thresholds.

Conclusions:

• The proposed model effectively explains why top hot news persists longer and why subsequent influential news struggles to gain similar focus.
• The findings offer practical strategies for online platforms to manage and prioritize information.
• The model provides a flexible framework for analyzing large-scale information transmission without excessive complexity.