Evidence map›Paper›PMID 35564591›Full record

ArticleInternational journal of environmental research and public health2022

Social Media User Behavior and Emotions during Crisis Events.

Mingyun Gu, Haixiang Guo, Jun Zhuang, Yufei Du, Lijin Qian

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

5 authors.

Mingyun GuCollege of Economics and Management, China University of Geosciences, Wuhan 430074, China.
Haixiang GuoCollege of Economics and Management, China University of Geosciences, Wuhan 430074, China.
Jun ZhuangDepartment of Industrial and Systems Engineering, School of Engineering and Applied Sciences, University at Buffalo, SUNY 317 Bell Hall, Buffalo, NY 14260, USA.ORCID 0000-0003-4830-6570
Yufei DuCollege of Economics and Management, China University of Geosciences, Wuhan 430074, China.
Lijin QianCollege of Economics and Management, China University of Geosciences, Wuhan 430074, China.ORCID 0000-0003-3496-8573

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The wide availability of smart mobile devices and Web 2.0 services has allowed people to easily access news, spread information, and express their opinions and emotions using various social media platforms. However, because of the ease of joining these sites, people also use them to spread rumors and vent their emotions, with the social platforms often playing a facilitation role. This paper collected more than 190,000 messages published on the Chinese Sina-Weibo platform to examine social media user behaviors and emotions during an emergency, with a particular research focus on the "Dr. Li Wenliang" reports associated with the COVID-19 epidemic in China. The verified accounts were found to have the strongest interactions with users, and the sentiment analysis revealed that the news from government agencies had a positive user effect and the national media and trusted experts were more favored by users in an emergency. This research provides a new perspective on trust and the use of social media platforms in crises, and therefore offers some guidance to government agencies.

Indexed as

COVID-19Social MediaChinaEmotionsHumansSARS-CoV-2COVID-19information disseminationsentiment analysissocial media

Identifiers

PMID35564591
PMCPMC9100990

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.