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

COVID-19 Vaccination (146) Infodemiology and Infoveillance (1349) Infoveillance and Social Listening (209) JMIR Theme Issue: COVID-19 Special Issue (2452) Natural Language Processing (692) Mass Media/Social Media Communication and Campaigns (461) Machine Learning (1465) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Vaccination and Immunization in the Digital Age (374) Hygiene and Infection Prevention (317) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Parent and Caregiver Education and Behavior Change for Vaccination (74) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 7, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29789, first published .
Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

Authors of this article:

Tau Ming Liew1, 2 Author Orcid Image ;   Cia Sin Lee3, 4 Author Orcid Image
Article Authors Cited by (30) Tweetations (9) Metrics

Journals

  1. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  2. Magazzino C, Mele M, Coccia M. A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality. Epidemiology and Infection 2022;150 View
  3. Dalkner N, Fleischmann E, Fellendorf F, Wagner-Skacel J, Schönthaler E, Bengesser S, Häussl A, Tietz S, Tmava-Berisha A, Lenger M, Reininghaus E. COVID-19 vaccination motivation and underlying believing processes: A comparison study between individuals with affective disorder and healthy controls. Frontiers in Psychology 2022;13 View
  4. Ng Q, Lim S, Yau C, Liew T. Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period. Vaccines 2022;10(9):1457 View
  5. Matenga T, Zulu J, Moonzwe Davis L, Chavula M. Motivating factors for and barriers to the COVID-19 vaccine uptake: A review of social media data in Zambia. Cogent Public Health 2022;9(1) View
  6. 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
  7. Portelli B, Scaboro S, Tonino R, Chersoni E, Santus E, Serra G. Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets. Journal of Medical Internet Research 2022;24(5):e35115 View
  8. Lim S, Ng Q, Xin X, Lim Y, Boon E, Liew T. Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period. International Journal of Environmental Research and Public Health 2022;19(21):13834 View
  9. Cascini F, Pantovic A, Al-Ajlouni Y, Failla G, Puleo V, Melnyk A, Lontano A, Ricciardi W. Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature. eClinicalMedicine 2022;48:101454 View
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  11. Ng Q, Yau C, Lim Y, Wong L, Liew T. Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts. Public Health 2022;213:1 View
  12. Bendezu-Quispe G, Benites-Meza J, Urrunaga-Pastor D, Herrera-Añazco P, Uyen-Cateriano A, Rodriguez-Morales A, Toro-Huamanchumo C, Hernandez A, Benites-Zapata V. Mass Media Use to Learn About COVID-19 and the Non-intention to Be Vaccinated Against COVID-19 in Latin America and Caribbean Countries. Frontiers in Medicine 2022;9 View
  13. 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
  14. Aljedaani W, Saad E, Rustam F, de la Torre Díez I, Ashraf I. Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends. Mathematics 2022;10(17):3199 View
  15. Schmid-Petri H, Bürger M, Schlögl S, Schwind M, Mitrović J, Kühn R. The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic. Media and Communication 2023;11(1):293 View
  16. 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
  17. Ng Q, Teo Y, Kiew C, Lim B, Lim Y, Liew T. Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022. Cyberpsychology, Behavior, and Social Networking 2023;26(8):621 View
  18. Jiang Y, Pang P, Wong D, Kan H. Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review. Applied Sciences 2023;13(22):12346 View
  19. Khan S, Biswas M, Shah Z. Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review. Human Vaccines & Immunotherapeutics 2023;19(3) View
  20. Clark E, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health and Surveillance 2024;10:e49185 View
  21. Ahammad T. Identifying hidden patterns of fake COVID-19 news: An in-depth sentiment analysis and topic modeling approach. Natural Language Processing Journal 2024;6:100053 View
  22. Huang X, Lin Z, Qin J, Yu D, Zhang F, Fang G, Chen X, He J, Cen P, Li M, Zhang R, Luo T, Jiang J, An S, Liang H, Ye L, Liang B. Willingness to accept monkeypox vaccine and its correlates among men who have sex with men in Southern China: a web-based online cross-sectional study. Frontiers in Public Health 2024;12 View
  23. McKinley C, Choi J, Luo Y. Expressing Uncertainty and Risk About the Mpox Outbreak: A Textual Analysis of Twitter Messaging. Communication Studies 2024;75(3):283 View
  24. Cameron D, Grage L, Van Wyck R, Edwards A, Chavez Mapaye J, Cheng A, Garcia G. Identifying trusted local sources and predicting behavior change pathways according to COVID-19 vaccination status: Results of a 2022 statewide survey of Alaskan adults. Vaccine 2024;42(10):2592 View
  25. Cheung L, Lau A, Lam K, Ng P. A Review of Environmental Factors for an Ontology-Based Risk Analysis for Pandemic Spread. COVID 2024;4(4):466 View
  26. Tarango García A, Lugo-Reyes S, Alvarez Cardona A. Análisis de sentimientos acerca de la inmunoterapia con alérgenos en Twitter mediante un modelo de procesamiento natural de lenguaje. Revista Alergia México 2024;71(1):8 View
  27. Singh S, Dhir S, Sushil . The emotions for COVID-19 vaccine: Insights from Twitter analytics about hesitancy and willingness for vaccination. Journal of Policy Modeling 2024;46(5):964 View
  28. Sasse K, Mahabir R, Gkountouna O, Crooks A, Croitoru A, Koh K. Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media. PLOS ONE 2024;19(6):e0301488 View
  29. Jerfy A, Selden O, Balkrishnan R. The Growing Impact of Natural Language Processing in Healthcare and Public Health. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2024;61 View