The prevalence of health misinformation on social media could significantly influence individuals’ health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China’s largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (β = 0.003, p < 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.
ChenLWangXPengT-Q.Nature and diffusion of gynecologic cancer-related misinformation on social media: analysis of tweets. J Med Internet Res. 2018; 20: e11515-e.
2.
LewandowskySUllrichEKHSeifertCMSchwarzNCookJ.Misinformation and its correction: continued influence and successful debiasing. Psychol Sci Public Interest. 2012; 13: 106–131.
3.
SouthwellBGThorsonEAShebleL.Misinformation among mass audiences as a focus for inquiry. In: SouthwellBGThorsonEAShebleL (eds). Misinformation and Mass Audiences. Austin, TX: University of Texas Press; 2018. pp. 1–11.
4.
ThorsonE.Belief echoes: the persistent effects of corrected misinformation. Polit Commun. 2015; 33: 460–480.
5.
GriffinRJDunwoodySNeuwirthK.Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ Res. 1999; 80: S230–S245.
6.
KimHKAhnJAtkinsonLKahlorLA.Effects of COVID-19 misinformation on information seeking, avoidance, and processing: a multicountry comparative study. Sci Commun. 2020; 42: 586–615.
7.
LoombaSde FigueiredoAPiatekSJde GraafKLarsonHJ.Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat Hum Behav. 2021; 5: 337–348.
8.
TasnimSHossainMMMazumderH.Impact of rumors and misinformation on COVID-19 in social media. J Prev Med Public Health. 2020; 53: 171–174.
9.
FungIC-HFuK-WChanC-HChanBSBCheungC-NAbrahamT, et al. Social media’s initial reaction to information and misinformation on Ebola, August 2014: facts and rumors. Public Health Rep. 2016; 131: 461–473.
ZhangDGuJShaoR.A cluster analysis of college students’ health information acquisition channel: active seeking and accidental exposure. Chin J Journal Commun. 2015; 5: 1–7.
12.
ZhaoXFanJBasnyatIHuB.Online health information seeking using “#COVID-19 patient seeking help” on Weibo in Wuhan, China: descriptive study. J Med Internet Res. 2020; 22: e22910-e.
13.
YangQWuS.Air pollution in China: health information seeking and protective behaviors. Health Promot Int. 2020; 35: 1495–1506.
World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard [Internet]. 2020 [cited 2020December4]. Available from: https://covid19.who.int
GanC.Gratifications for using social media: a comparative analysis of Sina Weibo and WeChat in China. Inf Dev. 2018; 34: 139–147.
18.
SuttonJSpiroESJohnsonBFitzhughSGibsonBButtsCT.Warning tweets: serial transmission of messages during the warning phase of a disaster event. Inf Commun Soc. 2014; 17: 765–787.
19.
YangQSangalangARooneyMMaloneyEEmerySCappellaJN.How Is marijuana vaping portrayed on YouTube? Content, features, popularity and retransmission of vaping marijuana YouTube videos. J Health Commun. 2018; 23: 360–369.
20.
BergerJ.Word of mouth and interpersonal communication: a review and directions for future research. J Consum Psychol. 2014; 24: 586–607.
21.
BergerJMilkmanKL.What makes online content viral?J Marketing Res. 2012; 49: 192–205.
22.
KimHSLeeSCappellaJNVeraLEmeryS.Content characteristics driving the diffusion of antismoking messages: implications for cancer prevention in the emerging public communication environment. J Natl Cancer Inst Monogr. 2013; 2013: 182–187.
23.
XiaLBechwatiNN.Word of mouse: the role of cognitive personalization in online consumer reviews. J Interact Advertising. 2008; 9: 3–13.
24.
HornikJShaanan SatchiRCesareoLPastoreA.Information dissemination via electronic word-of-mouth: good news travels fast, bad news travels faster. Comput Human Behav. 2015; 45: 273–280.
25.
BaekHAhnJChoiY.Helpfulness of online consumer reviews: readers’ objectives and review cues. Int J Electron Commer. 2014; 17(2): 99–126.
26.
BebbingtonKMacLeodCEllisonTMFayN.The sky is falling: evidence of a negativity bias in the social transmission of information. Evol Hum Behav. 2017; 38: 92–101.
27.
BleiDM.Probabilistic topic models. Communications of the ACM. 2012; 55: 77–84.
28.
RobertsMEStewartBMTingleyDLucasCLeder-LuisJGadarianSK, et al. Structural topic models for open-ended survey responses. Am J Pol Sci. 2014; 58: 1064–1082.
29.
HaselmayerMJennyM.Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Qual Quant. 2016; 51: 2623–2646.
30.
LiuB.Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. New York, NY: Cambridge University Press;2015.
31.
ZhangSWeiZWangYLiaoT.Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Gener Comput Syst. 2018; 81: 395–403.
32.
XuGYuZYaoHLiFMengYWuX.Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access. 2019; 7: 43749–43762.
33.
LiWGuoKShiYZhuLZhengY.DWWP: domain-specific new words detection and word propagation system for sentiment analysis in the tourism domain. Knowl Based Syst. 2018; 146: 203–214.
34.
ChenJBeckenSStanticB.Lexicon based chinese language sentiment analysis method. Comput Sci Inf Syst. 2019; 16: 639–655.
35.
HeilbronDC.Zero-altered and other regression-models for count data with added zeros. Biom J. 1994; 36: 531–547.
36.
TuW.Zero-inflated data. In: El-ShaarawiAHPiegorschWW (eds). Encyclopedia of Environmetrics. Chichester: John Wiley and Sons; 2002. pp.2387–2390.
37.
FoxJ.Applied Regression Analysis and Generalized Linear Models. Thousand Oaks, CA: Sage Publications; 2015.
38.
YesilovaAKaydanMBKayaY.Modeling insect-egg data with excess zeros using zero-inflated regression models. Hacet J Math Stat. 2010; 39: 273–282.
39.
DoHJLimC-GKimYJChoiH-J editors. Analyzing emotions in twitter during a crisis: a case study of the 2015 Middle East Respiratory Syndrome outbreak in Korea. In: International Conference on Big Data and Smart Computing (BigComp). Hong Kong, China; 18–20 January 2016.
40.
FungIC-HTseZTHCheungC-NMiuASFuK-W. Ebola and the social media. Lancet. 2014; 384: 2207.
41.
ImranASDaudpotaSMKastratiZBatraR.Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets. IEEE Access. 2020; 8: 181074–181090.
42.
PicklesKCvejicENickelBCoppTBonnerCLeaskJ, et al. COVID-19 misinformation trends in Australia: prospective longitudinal national survey. J Med Internet Res. 2021; 23: e23805-e.
43.
BastaniPBahramiMA. COVID-19 related misinformation on social media: a qualitative study from Iran. J Med Internet Res [Internet]. Epub ahead of print April 2020 [cited 2021 June 25]. Available from: https://pubmed.ncbi.nlm.nih.gov/32250961/
44.
IslamMSSarkarTKhanSHMostofa KamalA-HHasanSMMKabirA, et al. COVID-19 related infodemic and Its impact on public health: a global social media analysis. Am J Trop Med Hyg. 2020; 103: 1621–1629.
LuoEZhangDLuoHLiuBZhaoKZhaoY, et al. Treatment efficacy analysis of traditional Chinese medicine for novel coronavirus pneumonia (COVID-19): an empirical study from Wuhan, Hubei Province, China. Chin Med. 2020; 15: 1–13.
47.
YangY.Use of herbal drugs to treat COVID-19 should be with caution. Lancet. 2020; 395: 1689–1690.
48.
BowlesJLarreguyHLiuS.Countering misinformation via WhatsApp: preliminary evidence from the COVID-19 pandemic in Zimbabwe. PLoS One. 2020; 15: e0240005-e.
49.
WongCMLJensenO.The paradox of trust: perceived risk and public compliance during the COVID-19 pandemic in Singapore. J Risk Res. 2020; 23: 1021–1030.
50.
ApukeODOmarB.Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telemat Inform. 2021; 56: 101475.
51.
PennycookGMcPhetresJZhangYLuJGRandDG.Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci. 2020; 31: 770–780.
52.
GuptaLGasparyanAYMisraDPAgarwalVZimbaOYessirkepovM.Information and misinformation on COVID-19 a cross-sectional survey study. J Korean Med Sci. 2020; 35: e256-e.
53.
YangQTuftsCUngarLGuntukuSMerchantR.To retweet or not to retweet: understanding what features of cardiovascular tweets influence their retransmission. J Health Commun. 2018; 23: 1026–1035.
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