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

Big Data (240) Infodemiology and Infoveillance (1349) Machine Learning (1465) Vaccination and Immunization in the Digital Age (374) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Discourse (38)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29584, first published .
Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse

Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse

Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse

Authors of this article:

Per E Kummervold1 Author Orcid Image ;   Sam Martin2, 3, 4, 5 Author Orcid Image ;   Sara Dada4, 6 Author Orcid Image ;   Eliz Kilich4 Author Orcid Image ;   Chermain Denny7 Author Orcid Image ;   Pauline Paterson4, 8 Author Orcid Image ;   Heidi J Larson4, 8, 9, 10 Author Orcid Image
Article Authors Cited by (20) Tweetations (14) Metrics

Journals

  1. Dowrick A, Mitchinson L, Hoernke K, Mulcahy Symmons S, Cooper S, Martin S, Vanderslott S, Vera San Juan N, Vindrola‐Padros C. Re‐ordering connections: UK healthcare workers' experiences of emotion management during the COVID‐19 pandemic. Sociology of Health & Illness 2021;43(9):2156 View
  2. Cheatham S, Kummervold P, Parisi L, Lanfranchi B, Croci I, Comunello F, Rota M, Filia A, Tozzi A, Rizzo C, Gesualdo F. Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model. Frontiers in Public Health 2022;10 View
  3. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  4. Melton C, White B, Davis R, Bednarczyk R, Shaban-Nejad A. Fine-tuned Sentiment Analysis of COVID-19 Vaccine–Related Social Media Data: Comparative Study. Journal of Medical Internet Research 2022;24(10):e40408 View
  5. Usher K, Durkin J, Martin S, Vanderslott S, Vindrola-Padros C, Usher L, Jackson D. Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts. Journal of Medical Internet Research 2021;23(10):e29025 View
  6. Sauvayre R, Vernier J, Chauvière C. An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach. JMIR Medical Informatics 2022;10(5):e37831 View
  7. Blane J, Bellutta D, Carley K. Social-Cyber Maneuvers During the COVID-19 Vaccine Initial Rollout: Content Analysis of Tweets. Journal of Medical Internet Research 2022;24(3):e34040 View
  8. Sun Y, Gao D, Shen X, Li M, Nan J, Zhang W. Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study. JMIR Medical Informatics 2022;10(4):e35606 View
  9. 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
  10. Müller M, Salathé M, Kummervold P. COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter. Frontiers in Artificial Intelligence 2023;6 View
  11. 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
  12. Vera San Juan N, Martin S, Badley A, Maio L, Gronholm P, Buck C, Flores E, Vanderslott S, Syversen A, Symmons S, Uddin I, Karia A, Iqbal S, Vindrola-Padros C. Frontline Health Care Workers’ Mental Health and Well-Being During the First Year of the COVID-19 Pandemic: Analysis of Interviews and Social Media Data. Journal of Medical Internet Research 2023;25:e43000 View
  13. Ullah N, Martin S, Poduval S. A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis. JMIR Formative Research 2024;8:e51152 View
  14. Bhatia T, Rathi S, Singh T, Naha B. Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools. EAI Endorsed Transactions on Pervasive Health and Technology 2024;10 View
  15. Chepo M, Martin S, Déom N, Khalid A, Vindrola-Padros C. Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study. Journal of Medical Internet Research 2024;26:e50139 View
  16. Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine 2024;154:102900 View
  17. Deiner M, Honcharov V, Li J, Mackey T, Porco T, Sarkar U. Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study. JMIR Infodemiology 2024;4:e59641 View
  18. Trevena W, Zhong X, Alvarado M, Semenov A, Oktay A, Devlin D, Gohil A, Chittimouju S. Utilizing Large Language Models to Detect and Understand Drug Discontinuation Events in Online Forums: Development and Validation Study (Preprint). Journal of Medical Internet Research 2023 View

Books/Policy Documents

  1. Kanojia D, Joshi A. Computational Intelligence Applications for Text and Sentiment Data Analysis. View