Evidence map›Paper›PMID 36034623›Full record

ArticleJournal of environmental and public health2022

Public View of Public Health Emergencies Based on Artificial Intelligence Data.

Shitao Zhang, Chun Chu-Ke, Hyunjoo Kim, Changqiang Jing

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
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  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Shitao ZhangSchool of Network Communication, Zhejiang YueXiu University, Shaoxing 312000, China.
Chun Chu-KeSchool of Network Communication, Zhejiang YueXiu University, Shaoxing 312000, China.
Hyunjoo KimSchool of Media and Communication, Kwangwoon University, Seoul 01897, Republic of Korea.
Changqiang JingDepartment of Inform, Linyi University, Linyi 276000, China.ORCID 0000-0003-3480-5029
Zhejiang Yuexiu UniversityKwangwoon University · KRLinyi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people's health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people's hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO's optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

Indexed as

COVID-19Public HealthArtificial IntelligenceEmergenciesHumansPublic Opinion

Identifiers

PMID36034623
PMCPMC9410812
OpenAlexW4290096677

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.