Evidence map›Paper›PMID 35085257›Full record

ArticlePloS one

Using Sina-Weibo microblogs to inform the development and dissemination of health awareness material about Zika virus transmission, China, 2016-17.

Qian Hou, Yueqiao Zhao, Xiaoge Su, Rong Rong, Sujian Situ, Ying Cui

Abstract read
In one paragraph

Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

6 authors.

Qian HouHealth Communication Center (Health Hotline Management Office), China Centers for Disease Control and Prevention, Beijing, China.
Yueqiao ZhaoHealth Communication Center (Health Hotline Management Office), China Centers for Disease Control and Prevention, Beijing, China.
Xiaoge SuHealth Communication Center (Health Hotline Management Office), China Centers for Disease Control and Prevention, Beijing, China.
Rong RongHealth Communication Center (Health Hotline Management Office), China Centers for Disease Control and Prevention, Beijing, China.
Sujian SituDivision of Global Health Protection, United States Centers for Disease Control and Prevention, Beijing, China.
Ying CuiHealth Communication Center (Health Hotline Management Office), China Centers for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOn 1 February 2016, the World Health Organization declared Zika transmission a public health emergency of international concern. Monitoring and responding to community awareness, concern, and possible knowledge gaps are critical during public health emergencies. Here, we describe the review and analysis of micro-blogs posted on Sina-Weibo, China's largest social media platform, to develop and disseminate a Zika virus education campaign.

methodsWe used CYYUN Voice Express' Weibo Spider tool and the search terms of "Zhaika" OR "Zika" OR "Zikv" to capture microblogs about Zika virus retrospectively from February 1 to December 31, 2016 and prospectively from June 1 to November 15, 2017. We described microblogs meeting our inclusion criteria by month and Zika virus outbreaks in Asia and by source (e.g., government agency, individual, or other). We identified common misleading or inaccurate content authored by individual micro-bloggers (i.e., not supported by available scientific evidence) through a qualitative review. We used this information to develop and disseminate health awareness material about the Zika virus through China CDC's 12320 Health Hotline Weibo account. An online survey was conducted to obtain feedback on the material.

resultsWe captured 15,888 microblogs meeting our inclusion criteria. Zika-related microblogs peaked in September 2016, corresponding to news reports about the Zika outbreak in Singapore (August to November 2016). Most microblogs (12,994 [82%]) were authored by individual users, followed by media agencies (842 [5%]), businesses (829 [5%]), international organizations (370 [2%]), and Chinese government agencies (235 [1%]). Relevant microblogs primarily focused on clinical symptoms and health risks, modes of transmission, and actions taken by individuals to prevent infection and seek health care. Incorrect and/or mis-leading information from individual users concentrated on modes of transmission and possible treatments. The microblog "#Zika is that far and this close" health campaign was posted on Sina-Weibo and Baidu (Internet search engine in China) on September 18, 2016. Younger respondents (p-value = 0.01), and those with at least a college education (p-value = 0.03), were more likely than other respondents to consider the online campaign reliable and trustworthy.

conclusionRoutine review of Sina-Weibo and other social media platforms could enhance the ability of public health staff to effectively respond to community concerns and awareness during public health emergencies. Advancements of social media monitoring tools and staff training could help to promote health awareness during emergencies by directly addressing public perceptions and concerns. Various approaches may be needed to reach different at-risk populations, particularly older and less educated populations who may prefer more traditional modes of communication.

Indexed as

BloggingChinaHealth Knowledge, Attitudes, PracticeHealth PromotionHumansPilot ProjectsPublic HealthQualitative ResearchRetrospective StudiesSocial MediaZika Virus Infection

Identifiers

PMID35085257
PMCPMC8794198

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.