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dc.contributor.authorZhang, Xiangliang
dc.contributor.authorYang, Qiang
dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorLyu, Xiaoting
dc.contributor.authorAlamro, Hind
dc.contributor.authorSalhi, Adil
dc.contributor.authorMa, Changsheng
dc.contributor.authorAlshehri, Manal
dc.contributor.authorJaber, Inji Ibrahim
dc.contributor.authorTifratene, Faroug
dc.contributor.authorWang, Wei
dc.contributor.authorGojobori, Takashi
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorGao, Xin
dc.identifier.citationZhang, X., Yang, Q., Albaradei, S., Lyu, X., Alamro, H., Salhi, A., … Gao, X. (2021). Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic. Humanities and Social Sciences Communications, 8(1). doi:10.1057/s41599-021-00798-7
dc.description.abstractAbstractSocial media (e.g., Twitter) has been an extremely popular tool for public health surveillance. The novel coronavirus disease 2019 (COVID-19) is the first pandemic experienced by a world connected through the internet. We analyzed 105+ million tweets collected between March 1 and May 15, 2020, and Weibo messages compiled between January 20 and May 15, 2020, covering six languages (English, Spanish, Arabic, French, Italian, and Chinese) and represented an estimated 2.4 billion citizens worldwide. To examine fine-grained emotions during a pandemic, we built machine learning classification models based on deep learning language models to identify emotions in social media conversations about COVID-19, including positive expressions (optimistic, thankful, and empathetic), negative expressions (pessimistic, anxious, sad, annoyed, and denial), and a complicated expression, joking, which has not been explored before. Our analysis indicates a rapid increase and a slow decline in the volume of social media conversations regarding the pandemic in all six languages. The upsurge was triggered by a combination of economic collapse and confinement measures across the regions to which all the six languages belonged except for Chinese, where only the latter drove conversations. Tweets in all analyzed languages conveyed remarkably similar emotional states as the epidemic was elevated to pandemic status, including feelings dominated by a mixture of joking with anxious/pessimistic/annoyed as the volume of conversation surged and shifted to a general increase in positive states (optimistic, thankful, and empathetic), the strongest being expressed in Arabic tweets, as the pandemic came under control.
dc.description.sponsorshipThe research reported in this publication was supported by funding from Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Saudi Arabia, with award numbers FCC/1/1976-17-01, FCC/1/1976-18-01, FCC/1/1976-19-01, FCC/1/1976-23-01, FCC/1/1976-24-01, FCC/1/1976-25-01, FCC/1/1976-26-01 and FCC/1/1976-31-01. We thank the KAUST writing support team for the proofread of our manuscript.
dc.publisherSpringer Science and Business Media LLC
dc.rightsArchived with thanks to Humanities and Social Sciences Communications
dc.titleRise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic
dc.contributor.departmentBiological and Environmental Science and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputational Biosciences Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentIT Planning & Project Management Office
dc.contributor.departmentInformation Technology
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalHumanities and Social Sciences Communications
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Computer Science and Information Technology, Beijing Jiaotong University, Beijing, China
kaust.personZhang, Xiangliang
kaust.personYang, Qiang
kaust.personAlbaradei, Somayah
kaust.personAlamro, Hind
kaust.personSalhi, Adil
kaust.personMa, Changsheng
kaust.personAlshehri, Manal
kaust.personJaber, Inji Ibrahim
kaust.personTifratene, Faroug
kaust.personGojobori, Takashi
kaust.personDuarte, Carlos M.
kaust.personGao, Xin
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: gitdevqiang/SenWave: The public tweets sentimental analysis dataset SenWave for Covid-19 research. Publication Date: 2020-06-14. github: <a href="" >gitdevqiang/SenWave</a> Handle: <a href="" >10754/669525</a></a></li></ul>
kaust.acknowledged.supportUnitComputational Bioscience Research Center (CBRC)

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