Evidence map›Paper›PMID 34832009›Full record

ArticleInternational journal of environmental research and public health2021

Analysis of the Evolution of User Emotion and Opinion Leaders' Information Dissemination Behavior in the Knowledge Q&A Community during COVID-19.

Xu Xu, Zhigang Li, Rui Wang, Li Zhao

Open access · goldAbstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

2 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 2 institutions in 1 country.

Xu XuSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China.
Zhigang LiSchool of Management Science, Chengdu University of Technology, Chengdu 610059, China.
Rui WangSchool of Management Science, Chengdu University of Technology, Chengdu 610059, China.
Li ZhaoSchool of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China.
Chengdu University of Technology · CNSouthwestern University of Finance and Economics · CN

Funding

Major Projects of Sichuan Province in the "13th Five-Year Plan" for Social Sciences in 2020 SC20YJ002
6 · The paper itself

Abstract

Since its emergence in 2019, COVID-19 has quickly triggered widespread public discussion on social media. From 26 February 2020 to 26 September 2020, this study collected data on COVID-19-related posts in the knowledge Q&A community, identified 220 opinion leaders of this community, and used social network analysis and sentiment analysis to analyze the information exchange behavior and emotional evolution of the opinion leaders during COVID-19. The results show that the COVID-19 topic community could be divided into seven main categories. The information dissemination of opinion leader information dissemination network had low efficiency, multiple paths, and a high degree of control. In addition, the emotional evolution of users showed obvious phased characteristics. User emotion changed from initially strong negative to strong positive over the course of the pandemic and eventually tended to be objective and neutral as time passed and the event stabilized.

Indexed as

COVID-19Social MediaEmotionsHumansInformation DisseminationSARS-CoV-2emotional evolutionessence postsinformation dissemination networkknowledge Q&A communityopinion leadersocial network analysis

Identifiers

PMID34832009
PMCPMC8618384
OpenAlexW3217578969

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.