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

• Published in: Healthcare informatics research Vol. 28; no. 4; pp. 307 - 318
• Main Authors: Quach, Ha-Linh, Pham, Thai Quang, Hoang, Ngoc-Anh, Phung, Dinh Cong, Nguyen, Viet-Cuong, Le, Son Hong, Le, Thanh Cong, Le, Dang Hai, Dang, Anh Duc, Tran, Duong Nhu, Ngu, Nghia Duy, Vogt, Florian, Nguyen, Cong-Khanh
• Format: Journal Article
• Language: English
• Published: Korean Society of Medical Informatics 01.10.2022; The Korean Society of Medical Informatics
• Subjects: covid-19; infodemic; Original; sentiment analysis; social media; vietnam
• ISSN: 2093-369X; 2093-3681; 2093-369X
• DOI: 10.4258/hir.2022.28.4.307

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.