Healthc Inform Res.  2022 Oct;28(4):307-318. 10.4258/hir.2022.28.4.307.

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

• Quach HL
• Pham TQ
• Hoang NA
• Phung DC
• Nguyen VC
• Le SH
• Le TC
• Le DH
• Dang AD
• Tran DN
• Ngu ND
• Vogt F
• Nguyen CK

• KMID: 2535806
• DOI: http://doi.org/10.4258/hir.2022.28.4.307

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.

Keyword

Sentiment Analysis; Social Media; Infodemic; COVID-19; Vietnam

Figure

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  Figure 1 (A) Epidemic curve of the COVID-19 epidemic in Vietnam from June to August 2020. The shaded area indicates the outbreak period in Da Nang. (B) Epidemic curve of the COVID-19 outbreak in Da Nang, Vietnam, from July 25 to August 31, 2020. COVID-19: coronavirus disease 2019.

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  Figure 2 Distribution of online information across the time periods of the outbreak stratified by the five NPI-related topics. NPI: non-pharmacological intervention, COVID-19: coronavirus disease 2019.

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  Figure 3 Distribution of positive and negative words used in 500 selected online posts stratified by posts’ characteristics.

Reference

References

1. World Health Organization. Fighting misinformation in the time of COVID-19, one click at a time [Internet]. Geneva, Switzerland: World Health Organization;2021. [cited 2022 Sep 30]. Available from: https://www.who.int/news-room/feature-stories/detail/fighting-misinformation-in-the-time-of-covid-19-one-click-at-a-time .

2. World Health Organization. Understanding the infodemic and misinformation in the fight against COVID-19 [Internet]. Geneva, Switzerland: World Health Organization;2020. [cited 2022 Sep 30]. Available from: https://iris.paho.org/bitstream/handle/10665.2/52052/Factsheet-infodemic_eng.pdf?sequence=14 .

3. Tang L, Bie B, Park SE, Zhi D. Social media and outbreaks of emerging infectious diseases: a systematic review of literature. Am J Infect Control. 2018; 46(9):962–72. https://doi.org/10.1016/j.ajic.2018.02.010 .

4. Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, et al. Mass media and the contagion of fear: the case of Ebola in America. PLoS One. 2015; 10(6):e0129179. https://doi.org/10.1371/journal.pone.0129179 .

5. Takahashi B, Tandoc EC, Carmichael C. Communicating on Twitter during a disaster: an analysis of tweets during Typhoon Haiyan in the Philippines. Comput Human Behav. 2015; 50:392–8. https://doi.org/10.1371/journal.pone.0150190 .

6. World Health Organization. Managing the COVID-19 infodemic: promoting healthy behaviours and mitigating the harm from misinformation and disinformation [Internet]. Geneva, Switzerland: World Health Organization;2020. [cited 2022 Sep 30]. Available from: https://www.who.int/news/item/23-09-2020-managing-thecovid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation .

7. Quach HL, Nguyen KC, Hoang NA, Pham TQ, Tran DN, Le MT, et al. Association of public health interventions and COVID-19 incidence in Vietnam, January to December 2020. Int J Infect Dis. 2021; 110(Suppl 1):S28–S43. https://doi.org/10.1016/j.ijid.2021.07.044 .

8. Le TH, Tran TP. Alert for COVID-19 second wave: a lesson from Vietnam. J Glob Health. 2021; 11:03012. https://doi.org/10.7189/jogh.11.03012 .

9. Internet World Stats. Top 20 countries in Internet users [Internet]. [place unknown]: Internet World Stats;2019. [cited 2022 Sep 30]. Available from: https://www.internetworldstats.com/top20.htm .

10. Chou WS, Oh A, Klein WM. Addressing health-related misinformation on social media. JAMA. 2018; 320(23):2417–8. https://doi.org/10.1001/jama.2018.16865 .

11. Cuan-Baltazar JY, Munoz-Perez MJ, Robledo-Vega C, Perez-Zepeda MF, Soto-Vega E. Misinformation of COVID-19 on the Internet: infodemiology study. JMIR Public Health Surveill. 2020; 6(2):e18444. https://doi.org/10.2196/18444 .

12. Ferrara E, Cresci S, Luceri L. Misinformation, manipulation, and abuse on social media in the era of COVID-19. J Comput Soc Sci. 2020; 3(2):271–7. https://doi.org/10.1007/s42001-020-00094-5 .

13. Alamoodi AH, Zaidan BB, Al-Masawa M, Taresh SM, Noman S, Ahmaro IY, et al. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Comput Biol Med. 2021; 139:104957. https://doi.org/10.1016/j.compbiomed.2021.104957 .

14. Alonso MA, Vilares D, Gomez-Rodríguez C, Vilares J. Sentiment analysis for fake news detection. Electronics. 2021; 10(11):1348. https://doi.org/10.3390/electronics10111348 .

15. Zaeem RN, Li C, Barber KS. On sentiment of online fake news. In : Proceedings of 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 2020 Dec 7–10; The Hague, Netherlands. p. 760–7. https://doi.org/10.1109/ASONAM49781.2020.9381323 .

16. Ahinkorah BO, Ameyaw EK, Hagan JE, Seidu AA, Schack T. Rising above misinformation or fake news in Africa: another strategy to control COVID-19 spread. Front Commun. 2020; 5:45. https://doi.org/10.3389/fcomm.2020.00045 .

17. Hou Z, Du F, Zhou X, Jiang H, Martin S, Larson H, et al. Cross-country comparison of public awareness, rumors, and behavioral responses to the COVID-19 epidemic: infodemiology Study. J Med Internet Res. 2020; 22(8):e21143. https://doi.org/10.2196/21143 .

18. Enders AM, Uscinski JE, Klofstad C, Stoler J. The different forms of COVID-19 misinformation and their consequences. Harv Kennedy Sch Misinformation Rev. 2020; 1(8):1–21.

19. World Health Organization. WHO public health research agenda for managing infodemics [Internet]. Geneva, Switzerland: World Health Organization;2021. [cited 2022 Sep 30]. Available from: https://www.who.int/publications/i/item/9789240019508 .

20. Kouzy R, Abi Jaoude J, Kraitem A, El Alam MB, Karam B, Adib E, et al. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on twitter. Cureus. 2020; 12(3):e7255. https://doi.org/10.7759/cureus.7255 .

21. Nguyen-Nhat DK, Duong HT. One-document training for Vietnamese sentiment analysis. Tagarelli A, Tong H, editors. Computational data and social networks. Cham, Switzerland: Springer;2019. p. 189–200. https://doi.org/10.1007/978-3-030-34980-6_21 .

22. Vu T, Nguyen DQ, Nguyen DQ, Dras M, Johnson M. VnCoreNLP: a Vietnamese natural language processing toolkit. In : Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT); 2018 Jun 2–4; New Orleans, LA. p. 56–60.

23. Singh L, Bansal S, Bode L, Budak C, Chi G, Kawintiranon K, et al. A first look at COVID-19 information and misinformation sharing on Twitter. ArXiv [Preprint] 2020;arXiv:200313907v1.

24. Yang KC, Torres-Lugo C, Menczer F. Prevalence of low-credibility information on twitter during the covid- 19 outbreak [Internet]. Ithaca (NY): arXiv.org;2020. [cited 2022 Sep 30]. Available from: https://arxiv.org/abs/2004.14484 .

25. Gallotti R, Valle F, Castaldo N, Sacco P, De Domenico M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat Hum Behav. 2020; 4(12):1285–93. https://doi.org/10.1038/s41562-020-00994-6 .

26. Fung IC, Fu KW, Chan CH, Chan BS, Cheung CN, Abraham T, et al. Social media’s initial reaction to information and misinformation on Ebola, August 2014: facts and rumors. Public Health Rep. 2016; 131(3):461–73. https://doi.org/10.1177/003335491613100312 .

27. Shahi GK, Dirkson A, Majchrzak TA. An exploratory study of COVID-19 misinformation on Twitter. Online Soc Netw Media. 2021; 22:100104. https://doi.org/10.1016/j.osnem.2020.100104 .

28. Kim J, Yoo J. Role of sentiment in message propagation: reply vs. retweet behavior in political communication. In : Proceedings of 2012 International Conference on Social Informatics; 2012 Dec 14–16; Alexandria, VA. p. 131–6. https://doi.org/10.1109/SocialInformatics.2012.33 .

29. Stieglitz S, Dang-Xuan L. Political communication and influence through microblogging: an empirical analysis of sentiment in Twitter messages and retweet behavior. In : Proceedings of 2012, 45th Hawaii International Conference on System Sciences; 2012 Jan 4–7; Maui, HI. p. 3500–9. https://doi.org/10.1109/HICSS.2012.476 .

30. Medford RJ, Saleh SN, Sumarsono A, Perl TM, Lehmann CU. An “Infodemic”: leveraging high-volume twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak. Open Forum Infect Dis. 2020; 7(7):ofaa258. https://doi.org/10.1093/ofid/ofaa258 .