Evidence map›Paper›PMID 33175693›Full record

ArticleJMIR public health and surveillance2020

Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis.

Qing Xu, Ziyi Shen, Neal Shah, Raphael Cuomo, Mingxiang Cai, Matthew Brown, Jiawei Li, Tim Mackey

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
13.4field-weighted citation impact, top 1% 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

20 citing papers in PubMed, 48 citations in OpenAlex.

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  6. COVID-19 and Masking Disparities: Qualitative Analysis of Trust on the CDC's Facebook Page.International journal of environmental research and public health · 2023
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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

8 authors at 1 institution in 1 country.

Qing XuDepartment of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.ORCID 0000-0002-4507-1094
Ziyi ShenMasters Program in Computer Science, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States.ORCID 0000-0002-4284-2092
Neal ShahDepartment of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.ORCID 0000-0001-8670-6124
Raphael CuomoGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0002-8179-0619
Mingxiang CaiGlobal Health Policy and Data Institute, San Diego, CA, United States.ORCID 0000-0001-6816-5192
Matthew BrownUS Embassy, National Cancer Institute, National Institutes of Health, Beijing, China.ORCID 0000-0001-6535-4940
Jiawei LiDepartment of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.ORCID 0000-0001-9801-4715
Tim MackeyDepartment of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.ORCID 0000-0002-2191-7833
University of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.

objectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.

methodsWe used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19-related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.

resultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior.

conclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

Indexed as

Health Knowledge, Attitudes, PracticeChinaCOVID-19Data MiningHumansQualitative ResearchSARS-CoV-2Social MediaattitudebehaviorChinacontent analysisCOVID-19data mininginfodemicinfodemiologyinfoveillanceknowledgesocial mediaWeibo

Identifiers

PMID33175693
PMCPMC7722484
OpenAlexW3096311683

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.