Evidence map›Paper›PMID 35886225›Full record

ArticleInternational journal of environmental research and public health2022

Exploring the Chinese Public's Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis.

Han Wang, Kun Sun, Yuwei Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Omicron virus emotions understanding system based on deep learning architecture.Journal of ambient intelligence and humanized computing · 2023
    Article
  5. 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

3 authors.

Han WangSchool of Journalism and Communication, Jinan University, Guangzhou 510632, China.ORCID 0000-0001-7560-5395
Kun SunSchool of Journalism and Communication, Jinan University, Guangzhou 510632, China.ORCID 0000-0002-3945-1322
Yuwei WangSchool of Journalism and Communication, Jinan University, Guangzhou 510632, China.ORCID 0000-0002-7512-1750

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 is still raging. Similar to other RNA viruses, SARS-COV-2 is constantly mutating, which leads to the production of many infectious and lethal strains. For instance, the omicron variant detected in November 2021 became the leading strain of infection in many countries around the world and sparked an intense public debate on social media. The aim of this study is to explore the Chinese public's perception of the omicron variants on social media. A total of 121,632 points of data relating to omicron on Sina Weibo from 0:00 27 November 2021 to 23:59:59 30 March 2022 (Beijing time) were collected and analyzed with LDA-based topic modeling and DLUT-Emotion ontology-based sentiment analysis. The results indicate that (1) the public discussion of omicron is based on five topics, including omicron's impact on the economy, the omicron infection situation in other countries/regions, the omicron infection situation in China, omicron and vaccines and pandemic prevention and control for omicron. (2) From the 3 sentiment orientations of 121,632 valid Weibo posts, 49,402 posts were judged as positive emotions, accounting for approximately 40.6%; 47,667 were negative emotions, accounting for nearly 39.2%; and 24,563 were neutral emotions, accounting for about 20.2%. (3) The result of the analysis of the temporal trend of the seven categories of emotion attribution showed that fear kept decreasing, whereas good kept increasing. This study provides more insights into public perceptions of and attitudes toward emerging SARS-CoV-2 variants. The results of this study may provide further recommendations for the Chinese government, public health authorities, and the media to promote knowledge about SARS-CoV-2 variant pandemic-resistant messages.

Indexed as

COVID-19EmotionsLatent Class AnalysisPublic OpinionSARS-CoV-2Sentiment AnalysisSocial MediaAttitudeChinaCOVID-19 VaccinesFederal GovernmentHealth EducationHumansInternationalityPandemicsPublic HealthCOVID-19 VaccinesCOVID-19omicronsentiment analysissocial mediatopic modeling

Identifiers

PMID35886225
PMCPMC9319145

What OpenQuestion holds

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LicenceCC BY
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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.