Public authority control strategy for opinion evolution in social networks

Area of Science:

• Sociology
• Network Science
• Computational Social Science

Background:

• Controlling public opinion and managing rumors are critical societal challenges.
• Existing strategies like degree, random, and adaptive bridge control have limitations.
• The influence of authoritative figures in opinion dynamics is increasingly recognized.

Purpose of the Study:

• To propose and evaluate a novel public opinion control strategy based on public authority (PA).
• To investigate the effectiveness of the PA strategy across different social network topologies and PA distributions.
• To compare the PA strategy with existing control methods in terms of convergence time, controlled agents, and efficiency.

Main Methods:

• Utilized the HK model to simulate public opinion dynamics.
• Implemented a public authority (PA) control strategy, considering PA distribution (normal and power-law).
• Compared PA strategy with random and degree control strategies on small-world, scale-free, and random networks.

Main Results:

• The PA strategy demonstrated effectiveness across all tested network topologies (small-world, scale-free, random).
• Network topology and PA distribution significantly influenced the control outcomes.
• All network structures achieved comprehensive efficiency with the PA strategy, regardless of PA distribution.

Conclusions:

• The proposed public authority strategy is a viable and efficient method for public opinion and rumor control.
• High-authority individuals play a crucial role in accelerating consensus and controlling opinion dynamics.
• The effectiveness of control strategies is contingent upon network structure and the distribution of authority.