Dataset Information

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

ABSTRACT:

Objectives

Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020.

Methods

We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts.

Results

Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33-0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07-0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23-2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20-76.70) or unverified (OR = 5.03; 95% CI, 1.66-15.24).

Conclusions

Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online "infodemics" to inform public health responses.

SUBMITTER: Quach HL

PROVIDER: S-EPMC9672499 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

Quach Ha-Linh HL Pham Thai Quang TQ Hoang Ngoc-Anh NA Phung Dinh Cong DC Nguyen Viet-Cuong VC Le Son Hong SH Le Thanh Cong TC Le Dang Hai DH Dang Anh Duc AD Tran Duong Nhu DN Ngu Nghia Duy ND Vogt Florian F Nguyen Cong-Khanh CK

Healthcare informatics research 20221031 4

<h4>Objectives</h4>Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020.<h4>Methods</h4>We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, ...[more]

PMID: 36380428

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Dataset Information

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

Objectives

Methods

Results

Conclusions

Publications

Understanding the COVID-19 Infodemic: Analyzing User-Generated Online Information During a COVID-19 Outbreak in Vietnam.

Similar Datasets

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