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

Vaccination and Immunization in the Digital Age (378) Machine Learning (1495) Public Health Informatics (93) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Parent and Caregiver Education and Behavior Change for Vaccination (75) Mass Media/Social Media Communication and Campaigns (466) Infodemiology and Infoveillance (1369) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1663) Discourse (38)

Published on in Vol 7, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23105, first published .
Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study

Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study

Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study

Authors of this article:

Young Anna Argyris1 Author Orcid Image ;   Kafui Monu2 Author Orcid Image ;   Pang-Ning Tan1 Author Orcid Image ;   Colton Aarts3 Author Orcid Image ;   Fan Jiang3 Author Orcid Image ;   Kaleigh Anne Wiseley1 Author Orcid Image
Article Authors Cited by (15) Tweetations (7) Metrics

Journals

  1. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  2. Hagen L, Fox A, O'Leary H, Dyson D, Walker K, Lengacher C, Hernandez R. The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding. JMIR Infodemiology 2022;2(1):e34231 View
  3. Nyawa S, Tchuente D, Fosso-Wamba S. COVID-19 vaccine hesitancy: a social media analysis using deep learning. Annals of Operations Research 2024;339(1-2):477 View
  4. Segall R, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12(2):1 View
  5. Di Domenico G, Nunan D, Pitardi V. Marketplaces of Misinformation: A Study of How Vaccine Misinformation Is Legitimized on Social Media. Journal of Public Policy & Marketing 2022;41(4):319 View
  6. Upadhyaya A, Fisichella M, Nejdl W. Towards sentiment and Temporal Aided Stance Detection of climate change tweets. Information Processing & Management 2023;60(4):103325 View
  7. Fasce A, Schmid P, Holford D, Bates L, Gurevych I, Lewandowsky S. A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling. Nature Human Behaviour 2023;7(9):1462 View
  8. Kurt O, Küçükkelepçe O, Öz E, Doğan Tiryaki H, Parlak M. Childhood Vaccine Attitude and Refusal among Turkish Parents. Vaccines 2023;11(8):1285 View
  9. Schlicht I, Fernandez E, Chulvi B, Rosso P. Automatic detection of health misinformation: a systematic review. Journal of Ambient Intelligence and Humanized Computing 2024;15(3):2009 View
  10. Nelson V, Bashyal B, Tan P, Argyris Y. Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance. Social Science & Medicine 2024;348:116775 View
  11. Kumi S, Snow C, Lomotey R, Deters R. Uncovering Concerns of Citizens Through Machine Learning and Social Network Sentiment Analysis. IEEE Access 2024;12:94885 View
  12. Nielsen J, Chen X, Davis L, Waterman A, Gentili M. Classification of Living Kidney Donation Experiences on Reddit: Understanding the Sensitivity of ChatGPT to Prompt Engineering (Preprint). JMIR AI 2024 View

Books/Policy Documents

  1. Segall R. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. View
  2. Hossain S, Sulaiman S. Advances on Intelligent Informatics and Computing. View