Evidence map›Paper›PMID 35486529›Full record

ArticleJMIR public health and surveillance2022

Cross-Verification of COVID-19 Information Obtained From Unofficial Social Media Accounts and Associated Changes in Health Behaviors: Web-Based Questionnaire Study Among Chinese Netizens.

Peiyi Li, Bo Chen, Genevieve Deveaux, Yunmei Luo, Wenjuan Tao, Weimin Li, Jin Wen, Yuan Zheng

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 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.

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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.

Peiyi LiDepartment of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-0177-6970
Bo ChenInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-4079-0759
Genevieve DeveauxMilken Institute School of Public Health, George Washington University, Washington, DC, United States.ORCID 0000-0002-1389-3915
Yunmei LuoWest China Medical Publishers, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-2661-3214
Wenjuan TaoInstitute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-5926-2545
Weimin LiDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-0985-0311
Jin Wen *Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-1502-7244
Yuan Zheng *Publicity Department, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-1915-0821

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics.

objectiveThis study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission.

methodsAn online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens' knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification.

resultsRespondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ≥50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non-health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities' social media accounts with a trust rating score greater than 1 (P≤.04) were associated with a lack of cross-verification of information.

conclusionsThe findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities' star power, and increase Chinese netizens' awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information.

Indexed as

COVID-19Social MediaAdultChinaCross-Sectional StudiesFemaleHealth BehaviorHumansMiddle AgedSARS-CoV-2Surveys and Questionnairesbehavior changeCOVID-19eHealth literacyinformation cross-verificationpandemicsocial media

Identifiers

PMID35486529
PMCPMC9198829

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.