Rumor propagation dynamic analysis model based on hypergraph integration in public health events.

Area of Science:

• Social Network Analysis
• Computational Social Science
• Information Science

Background:

• Online rumors during public health emergencies amplify panic and anxiety.
• Effective management of public opinion requires precise regulation of cyberspace rumor dissemination.
• Understanding rumor propagation patterns is crucial for administrators and researchers.

Purpose of the Study:

• To accurately describe rumor information propagation patterns from the source.
• To develop a novel model that reflects real-world rumor dynamics.
• To analyze factors influencing rumor forwarding mechanisms in social networks.

Main Methods:

• Construction of a novel H-SNIR (Hypernetwork-Susceptible-Neglected-Infectious-Recovered) model within a hypernetwork framework.
• Categorization of rumor spreaders into ordinary and hesitant types, incorporating psychological differences.
• Integration of user influence, topic popularity, and user interaction to analyze forwarding mechanisms.

Main Results:

• The H-SNIR model provides a more realistic representation of rumor propagation by including neglected spreaders.
• Analysis reveals the significant impact of user influence, topic popularity, and user interaction on rumor dissemination.
• The model enhances the understanding of how psychological differences affect rumor spread.

Conclusions:

• The H-SNIR model offers a valuable tool for studying online rumor propagation during crises.
• Findings contribute to improving public opinion management strategies in the digital age.
• Further research can leverage this model to predict and mitigate the negative impacts of misinformation.