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

Vaccination Sentiment and Anti-Vaccination Infodemiology (70) Infodemiology and Infoveillance (1449) JMIR Theme Issue: COVID-19 Special Issue (2493) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1087)

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 (21) 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
  14. Taubert F, Meyer-Hoeven G, Schmid P, Gerdes P, Betsch C. Conspiracy narratives and vaccine hesitancy: a scoping review of prevalence, impact, and interventions. BMC Public Health 2024;24(1) View
  15. Paradise Vit A, Magid A. Exploring Topics, Emotions, and Sentiments in Health Organization Posts and Public Responses on Instagram: Content Analysis. JMIR Infodemiology 2025;5:e70576 View
  16. Furqon I, Soyusiawaty D. The Role of VADER and SentiWordNet Labeling in Naïve Bayes Accuracy for Sentiment Analysis of Rice Price Increases. Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) 2025;7(1):72 View

Books/Policy Documents

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

Conference Proceedings

  1. Dubey A, Gokhale S. 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects View
  2. Dubey A, Athina L, Gokhale S. 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). Is Twitter a News Source or a Social Platform: A Case Study of Covid-19 Vaccine Conversations View
  3. Hariscandra T, Utami S, Hidayanto A. 2023 Eighth International Conference on Informatics and Computing (ICIC). Exploring COVID-19 Vaccine Hesitancy Through Topic Modeling: A Systematic Literature Review View
  4. BabaAhmadi A, Sabzian A, AmirPour M, ShariatPanahi M. 2024 10th International Conference on Web Research (ICWR). Twitter/X Emotions Analysis on COVID-19 Vaccines: A Journey Through Few-Shot Learnin View