Evidence map›Paper›PMID 39543394›Full record

ArticleScientific reports2024

Modeling and simulation on the spreading dynamics of public opinion information in temporal group networks.

Jiakun Wang, Linru Mu, Liu Chun, Xiaotong Guo

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

Jiakun WangCollege of Economics and Management, Shandong University of Science and Technology, Qingdao, 266500, China. shandongwjk@163.com.
Linru MuCollege of Economics and Management, Shandong University of Science and Technology, Qingdao, 266500, China.
Liu ChunSchool of Economics and Management, China University of Petroleum, Qingdao, 266580, China.
Xiaotong GuoCollege of Economics and Management, Shandong University of Science and Technology, Qingdao, 266500, China.

Funding

Natural Science Foundation of Shandong Province ZR2021QG035Qingdao Social Science Planning and Research Project QDSKL2201115Shandong Social Science Planning and Research Project 22DGLJ027
6 · The paper itself

Abstract

In the new media environment, the constantly emerging social platforms further expand the channels for the propagation of public opinion. Under the framework of complex network theory and faced the needs of management practice, modeling the spreading dynamics of public opinion in temporal group networks is of great significance for understanding its spreading law and improving the governance system of cyberspace and the development of network science. Through analyzing the changes of group networks topology and the spreading rules of public opinion, the spreading model of public opinion in temporal group networks was proposed by coupling the two dynamic processes, and the spreading thresholds of public opinion in static and temporal group networks were derived respectively. Then, the spreading characteristics of public opinion under different network topology, as well as the influence of important parameters on public opinion spreading process were discussed with the help of simulation experiments. The research results indicated that the propagation of public opinion in static and temporal group networks exhibits both similar trends and differentiated characteristics; compared with Spreader, the propagation of public opinion in temporal group networks is more sensitive to Ignorant's behavior; both groups' and netizens' active probability have significant influence on public opinion propagation, but netizens' affects more. Based on the relevant results, this paper proposed a series of countermeasures such as, grading social platforms, strengthening relationship management between them and introducing time management systems, so as to promote the formation of a good network ecosystem and the modernization of the national governance system.

Indexed as

Group networksPublic opinion informationSimulation experimentsSpreading modelTemporal networks

Identifiers

PMID39543394
PMCPMC11564530

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