A Noise Based Medical Elites Silence Model and Public Health Opinion Distortion in Social Networks

  • Jianliang Wei
  • , Chi Qin
  • , Hao Ji
  • , Lingling Guo
  • , Jingjing Chen*
  • , Yingying Xu*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Under the impact of internet populism, internet violence, and other noises on the internet, medical elites, who have a professional background, did not intend to share their opinions on the internet. Thus, misinformation about health is increasingly prevalent. We roughly divided the users in social networks into ordinary users, medical elites, and super-influencers. In this paper, we propose a communication model of health information based on the improved Hegselmann-Krause (H-K) model. By conducting MATLAB-based simulation, the experimental results showed that network noise was an important factor that interfered with opinion propagation regarding health. The louder the noise is, the harder it is for health opinions within a group to reach a consensus. But even in a noisy environment, super-influencers could influence the overall cognition on public health in the social network fundamentally. When the super-influencers held positive opinions in public health, the medical elite keeping silent had a noise-tolerant effect on opinion communication in public health, and vice versa. Thus, three factors concerning noise control, the free information release of medical elites, and the positive position of super-influence are very important to form a virtuous information environment for public health.

Original languageEnglish
Article number791893
JournalFrontiers in Public Health
Volume9
DOIs
StatePublished - 14 Jan 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 16 - Peace, Justice and Strong Institutions
    SDG 16 Peace, Justice and Strong Institutions

Keywords

  • distortion
  • information noise
  • misinformation
  • opinion propagation
  • public health
  • social network

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