COVID-19 vaccines in twitter ecosystem: Analyzing perceptions and attitudes by sentiment and text analysis method.
Aim: An abundance of information and rumors pertaining to COVID-19 vaccines has disseminated extensively especially through Twitter. The primary objective of this study is to explore and analyze the prevailing perceptions and attitudes towards COVID-19 vaccines as manifested within the Twitter ecosy...
| Publicado en: | Journal of Public Health: From Theory to Practice (2198-1833) Vol. 33; no. 5; pp. 965 - 980 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184627285&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184627285 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21981833 NENI jtl: Journal of Public Health: From Theory to Practice (2198-1833) issn: 21981833 maglogo: N pubinfo: dt: May2025 vid: 33 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 184627285 10.1007/s10389-023-02078-x ppf: 965 ppct: 15 formats: tig: atl: COVID-19 vaccines in twitter ecosystem: Analyzing perceptions and attitudes by sentiment and text analysis method. aug: au: Kahraman, Elif Demirel, Sadettin Gündüz, Uğur affil: https://ror.org/03a5qrr21 Department of Corporate Communications, Istanbul University, Istanbul, Turkey https://ror.org/02dzjmc73 Faculty of Communication, Department of New Media and Communication, Uskudar University, Istanbul, Turkey https://ror.org/03a5qrr21 Faculty of Communication, Department of Journalism, Istanbul University, Istanbul, Turkey su: Twitter (Web resource) Immunization Health attitudes Vaccination Public opinion Misinformation Attitude (Psychology) Communication Vaccine hesitancy Sentiment analysis Statistical correlation Questionnaires COVID-19 vaccines Descriptive statistics Research Text messages Comparative studies COVID-19 sug: subj: Immunization Health attitudes Vaccination Public opinion Misinformation Attitude (Psychology) Communication Vaccine hesitancy Sentiment analysis Administration of Public Health Programs Wireless Telecommunications Carriers (except Satellite) Statistical correlation Questionnaires COVID-19 vaccines Descriptive statistics Research Text messages Comparative studies COVID-19 Twitter (Web resource) keyword: Health communication Text mining Tweetosphere Vaccine Health communication Text mining Tweetosphere Vaccine ab: Aim: An abundance of information and rumors pertaining to COVID-19 vaccines has disseminated extensively especially through Twitter. The primary objective of this study is to explore and analyze the prevailing perceptions and attitudes towards COVID-19 vaccines as manifested within the Twitter ecosystem. Subject and methods: The utilization of social media platforms for conducting public health analyses during pandemics has garnered heightened attention. Twitter, in particular, offers the potential to present trustworthy and real-time data regarding public opinions during crises, owing to the presence of verified accounts belonging to public health officials and authorities. This study employs a text mining methodology and sentiment analysis to examine a comprehensive dataset comprising 66,048 tweets. These tweets, posted between the 5th and 14th of October 2021, focus on four COVID-19 vaccines (AstraZeneca, Biontech, Sinovac and Sputnik5), with the aim of scrutinizing the prevailing perceptions and attitudes towards these vaccines within the Twitter community. Results: The results are presented as text and sentiment analysis. As a result of the text analysis, the efficacy and side effects of the vaccines are the main topics to be discussed. According to sentiment analysis, AstraZeneca and Biontech have more percentage of negative tweets associated with them whereas Sinovac and Sputnik5 have more percentage of positive tweets. Conclusion: The sentiment analysis of tweets regarding vaccines highlights the intricate relationship between the textual aspects and formal features of the tweets. Furthermore, it offers insights into the level of influence and dissemination exhibited by these tweets within the Twitter ecosystem. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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