Evidence map›Paper›PMID 34360053›Full record

ArticleInternational journal of environmental research and public health2021

How Does Risk-Information Communication Affect the Rebound of Online Public Opinion of Public Emergencies in China?

Shan Gao, Ye Zhang, Wenhui Liu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Generation Paths of Major Road Accidents Based on Fuzzy-Set Qualitative Comparative Analysis.International journal of environmental research and public health · 2022
    Review
  7. Article
  8. 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.

Shan GaoSchool of Public Administration, Central South University, Changsha 410083, China.
Ye ZhangSchool of Public Administration, Central South University, Changsha 410083, China.
Wenhui LiuSchool of Public Administration, Central South University, Changsha 410083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rebound of online public opinion is an important driving force in inducing a secondary crisis in the case of public emergencies. Effective risk-information communication is an important means to manage online public opinion regarding emergencies. This paper employs fuzzy-set qualitative comparative analysis to discover which conditions are combined and may result in the rebound of online public opinion. Five conditions were selected: the type of public emergency, messengers, message attributes, audience, and information feedback. The study used a sample of 25 major public emergencies that occurred between 2015 and 2020 in China. The type of public emergency, audience, and information feedback emerged as critical influencing factors. Message attributes promote the rebound of online public opinion regarding public health emergencies, while messengers play a traction role in the rebound of online public opinion on other types of public emergencies. This study extends risk-information communication theory from the perspective of the type of emergency, explores the causes of rebounded online public opinion regarding public emergencies, and provides policies and suggestions for risk-information communication and online public-opinion governance during emergencies.

Indexed as

EmergenciesPublic OpinionChinaCommunicationHumansPublic HealthfsQCApublic emergenciespublic health emergenciesrebound of online public opinionrisk-information communication

Identifiers

PMID34360053
PMCPMC8345355

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.