This paper is in the following e-collection/theme issue:

Vaccination Sentiment and Anti-Vaccination Infodemiology (58) Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33909, first published .
Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

Authors of this article:

Ranganathan Chandrasekaran1 Author Orcid Image ;   Rashi Desai1 Author Orcid Image ;   Harsh Shah1 Author Orcid Image ;   Vivek Kumar1 Author Orcid Image ;   Evangelos Moustakas2 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

Journals

  1. Ackleh-Tingle J, Jordan N, Onwubiko U, Chandra C, Harton P, Rentmeester S, Chamberlain A. Prevalence and Correlates of COVID-19 Vaccine Information on Family Medicine Practices’ Websites in the United States: Cross-sectional Website Content Analysis. JMIR Formative Research 2022;6(11):e38425 View
  2. Chandrasekaran R, Bapat P, Jeripity Venkata P, Moustakas E. Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews. Telemedicine and e-Health 2023;29(10):1557 View
  3. Park S, Suh Y. A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e42623 View
  4. Saini V, Liang L, Yang Y, Le H, Wu C. The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR Infodemiology 2022;2(1):e37077 View
  5. Lee C, Kong P, Yang W. Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models. Journal of Mathematics 2023;2023:1 View
  6. Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. Journal of Medical Internet Research 2023;25:e40057 View
  7. Lindelöf G, Aledavood T, Keller B. Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts. Journal of Medical Internet Research 2023;25:e41319 View
  8. Dupuy-Zini A, Audeh B, Gérardin C, Duclos C, Gagneux-Brunon A, Bousquet C. Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts. Journal of Medical Internet Research 2023;25:e37237 View
  9. Mitchell S, Beanlands J. “The mask is not for you” : A framing analysis of pro- and anti-mask sentiment on Twitter. Health & New Media Research 2022;6(1):3 View
  10. Chandrasekaran R, Bapat P, Venkata P, Moustakas E. Face time with physicians: How do patients assess providers in video-visits?. Heliyon 2023;9(6):e16883 View
  11. Shankar K, Chandrasekaran R, Jeripity Venkata P, Miketinas D. Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis. Journal of Medical Internet Research 2023;25:e47328 View
  12. Küçük D, Arıcı N. Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination. International Journal on Semantic Web and Information Systems 2023;19(1):1 View
  13. Yin S, Chen S, Ge Y. Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study. JMIR Infodemiology 2024;4:e49756 View

Books/Policy Documents

  1. Siewert S, Kieslich K, Braun M, Dabrock P. Synthetic Biology and the Question of Public Participation. View