Evidence map›Paper›PMID 36231895›Full record

ArticleInternational journal of environmental research and public health2022

Changes in Public Sentiment under the Background of Major Emergencies-Taking the Shanghai Epidemic as an Example.

Bowen Zhang, Jinping Lin, Man Luo, Changxian Zeng, Jiajia Feng, Meiqi Zhou, Fuying Deng

Open access · goldAbstract 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 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. 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

7 authors at 3 institutions in 1 country.

Bowen ZhangSchool of Earth Sciences, Yunnan University, Kunming 650000, China.
Jinping LinSchool of Earth Sciences, Yunnan University, Kunming 650000, China.
Man LuoSchool of Earth Sciences, Yunnan University, Kunming 650000, China.
Changxian ZengFaculty of Science, Dalian University for Nationalities, Dalian 116000, China.
Jiajia FengSchool of Earth Sciences, Yunnan University, Kunming 650000, China.
Meiqi ZhouSchool of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650000, China.
Fuying DengSchool of Earth Sciences, Yunnan University, Kunming 650000, China.
Yunnan University · CNDalian University · CNYunnan Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.

Indexed as

COVID-19Social MediaAttitudeChinaEmergenciesEmotionsHumansPandemicsCoupla entropyCOVID-19emotionsERNIE pre-training modelShanghai

Identifiers

PMID36231895
PMCPMC9565156
OpenAlexW4303696604

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.