Evidence map›Paper›PMID 37601226›Full record

ArticleFrontiers in public health2023

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government.

Qiaohe Zhang, Tianyue Niu, Jinhua Yang, Xiaochen Geng, Yinhuan Lin

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Qiaohe ZhangAcademy of Fine Arts, Huaibei Normal University, Huaibei, China.
Tianyue NiuAcademy of Arts & Design, Tsinghua University, Beijing, China.
Jinhua YangCollege of Humanities, Tongji University, Shanghai, China.
Xiaochen GengSchool of Art, Nantong University, Nantong, China.
Yinhuan LinXiamen Academy of Arts and Design, Fuzhou University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Since the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the "emergency" prevention and control policy provides a unique research perspective for this study. Methods: The purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions. Results: The results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes. Conclusion: The significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.

Indexed as

COVID-19Social MediaArtificial IntelligenceEmotionsHumansLocal GovernmentNatural Language ProcessingPandemicsPolicyartificial intelligenceCOVID-19deregulation of epidemic controlemotional and attitudinal behaviorsnatural language processingsocial media users

Identifiers

PMID37601226
PMCPMC10436617

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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.