Analysis and Insights for Myths Circulating on Twitter During the COVID-19 Pandemic

被引:18
|
作者
Yang, Shuiqiao [1 ]
Jiang, Jiaojiao [2 ]
Pal, Arindam [3 ,4 ]
Yu, Kun [1 ]
Chen, Fang [1 ]
Yu, Shui [5 ]
机构
[1] Univ Technol Sydney, Data Sci Inst, Sydney, NSW 2007, Australia
[2] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia
[3] CSIRO, Data61, Sydney, NSW 2122, Australia
[4] Cyber Secur CRC, Sydney, NSW 2122, Australia
[5] Univ Technol Sydney, Sch Comp Sci, Sydney, NSW 2007, Australia
关键词
COVID-19; myth; tweet; diffusion; emotion;
D O I
10.1109/OJCS.2020.3028573
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The current COVID-19 pandemic and its uncertainty have given rise to various myths and rumours. These myths spread incredibly fast through social media, which has caused massive panic in society. In this paper, we comprehensively examined the prevailing myths related to COVID-19 in regard to the diffusion of myths, people's engagement with myths and people's subjective emotions to myths. First, we classified the myths into five categories: spread of infection, preventive measures, detection measures, treatment and miscellaneous. We collected the tweets about each category of myths from 1 January to 7 July in the year 2020. We found that the vast majority of the myth tweets were about the spread of the infection. Next, we fitted myths spreading with the SIR epidemic model and calculated the basic reproduction number R-0 for each category of myths. We observed that the myths about the spread of infection and preventive measures propagated faster than other categories of myths, and more miscellaneous myths raised and quickly spread from later June 2020. We further analyzed people's emotions evoked by each category of myths and found that fear was the strongest emotion in all categories of myths and around 64% of the collected tweets expressed the emotion of fear. The study in this paper provides insights for authorities and governments to understand the myths during the eruption of the pandemic, and hence enable targeted and feasible measures to demystify the most concerned myths in due time.
引用
收藏
页码:209 / 219
页数:11
相关论文
共 50 条
  • [1] Diet during the COVID-19 pandemic: An analysis of Twitter data
    Hernandez, Mark A.
    Modi, Shagun
    Mittal, Kanisha
    Dwivedi, Pallavi
    Nguyen, Quynh C.
    Cesare, Nina L.
    Nsoesie, Elaine O.
    [J]. PATTERNS, 2022, 3 (08):
  • [2] Ageism on Twitter during the COVID-19 pandemic
    Ng, Reuben
    Indran, Nicole
    Liu, Luyao
    [J]. JOURNAL OF SOCIAL ISSUES, 2022, 78 (04) : 842 - 859
  • [3] Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic
    Claes M.
    Farooq U.
    Salman I.
    Teern A.
    Isomursu M.
    Halonen R.
    [J]. SN Computer Science, 5 (2)
  • [4] Analysis of COVID-19 and Rheumatology Twitter Activity During the Pandemic Months
    Mohameden, Mosaab
    Ali, H. Ali
    [J]. ARTHRITIS & RHEUMATOLOGY, 2020, 72
  • [5] Twitter discussions on breastfeeding during the COVID-19 pandemic
    Jawahar Jagarapu
    Marlon I. Diaz
    Christoph U. Lehmann
    Richard J. Medford
    [J]. International Breastfeeding Journal, 18
  • [6] Twitter discussions on breastfeeding during the COVID-19 pandemic
    Jagarapu, Jawahar
    Diaz, Marlon I.
    Lehmann, Christoph U.
    Medford, Richard J.
    [J]. INTERNATIONAL BREASTFEEDING JOURNAL, 2023, 18 (01)
  • [7] Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data
    Valdez, Danny
    ten Thij, Marijn
    Bathina, Krishna
    Rutter, Lauren A.
    Bollen, Johan
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (12)
  • [8] Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic
    Arpaci, Ibrahim
    Alshehabi, Shadi
    Al-Emran, Mostafa
    Khasawneh, Mahmoud
    Mahariq, Ibrahim
    Abdeljawad, Thabet
    Hassanien, Aboul Ella
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2020, 65 (01): : 193 - 203
  • [9] Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
    Joanna Sleigh
    Julia Amann
    Manuel Schneider
    Effy Vayena
    [J]. BMC Public Health, 21
  • [10] Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis
    Kwon, Jiye
    Grady, Connor
    Feliciano, Josemari T.
    Fodeh, Samah J.
    [J]. JOURNAL OF BIOMEDICAL INFORMATICS, 2020, 111