Evidence map›Paper›PMID 35096745›Full record

ArticleFrontiers in public health2021

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

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Jianliang WeiSchool of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China.
Chi QinSchool of Management Engineering and E-Commerce, Zhejiang Gongshang University, Hangzhou, China.
Hao JiSchool of Environment and Resource, Zhejiang Agriculture and Forestry University, Hangzhou, China.
Lingling GuoCollege of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China.
Jingjing ChenZhijiang College, Zhejiang University of Technology, Hangzhou, China.
Yingying XuSchool of Economics and Management, University of Science and Technology Beijing, Beijing, China.
Zhejiang Gongshang University · CNZhejiang University of Technology · CNUniversity of Science and Technology Beijing · CNZhejiang A & F University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Public HealthPublic OpinionAttitudeCommunicationSocial Networkingdistortioninformation noisemisinformationopinion propagationpublic healthsocial network

Identifiers

PMID35096745
PMCPMC8795678
OpenAlexW4206629644

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.