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

Infodemiology and Infoveillance (1269) Vaccination and Immunization in the Digital Age (343) Mass Media/Social Media Communication and Campaigns (446) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010) HPV Vaccination (51) Innovations and Technology for Cancer Prevention and Screening (137) Machine Learning (1370) Clinical Information and Decision Making (1314) Doctor-Patient Communication on Cancer, Prevention, and Screening (41) Parent and Caregiver Education and Behavior Change for Vaccination (70) Quality/Credibility of eHealth Information and Trust Issues (485)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30451, first published .
Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models

Authors of this article:

Tre Tomaszewski1 Author Orcid Image ;   Alex Morales2 Author Orcid Image ;   Ismini Lourentzou3 Author Orcid Image ;   Rachel Caskey4 Author Orcid Image ;   Bing Liu5 Author Orcid Image ;   Alan Schwartz6 Author Orcid Image ;   Jessie Chin1, 7 Author Orcid Image
Article Authors Cited by (10) Tweetations (9) Metrics

Journals

  1. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  2. Mønsted B, Lehmann S, Danforth C. Characterizing polarization in online vaccine discourse—A large-scale study. PLOS ONE 2022;17(2):e0263746 View
  3. Ota Y, Nomura K, Fujita N, Suzuki T, Kamatsuka M, Sakata N, Nagashima K, Hirayama J, Fujita N, Shiga K, Oyama N, Terada Y. Influence of LINE-Assisted Provision of Information about Human Papillomavirus and Cervical Cancer Prevention on HPV Vaccine Intention: A Randomized Controlled Trial. Vaccines 2022;10(12):2005 View
  4. Suzuki Y, Sukegawa A, Ueda Y, Sekine M, Enomoto T, Melamed A, Wright J, Miyagi E. The Effect of a Web-Based Cervical Cancer Survivor’s Story on Parents' Behavior and Willingness to Consider Human Papillomavirus Vaccination for Daughters: Randomized Controlled Trial. JMIR Public Health and Surveillance 2022;8(5):e34715 View
  5. Nyawa S, Tchuente D, Fosso-Wamba S. COVID-19 vaccine hesitancy: a social media analysis using deep learning. Annals of Operations Research 2022 View
  6. Thakur N, Han C. An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection. COVID 2022;2(8):1026 View
  7. Thakur N. Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing 2023;7(2):116 View
  8. Thakur N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Applied System Innovation 2023;6(5):92 View
  9. Ma S, Jiang S, Yang O, Zhang X, Fu Y, Zhang Y, Kaareen A, Ling M, Chen J, Shang C. Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation. JMIR Formative Research 2024;8:e49031 View
  10. Lee Y, Alostad H, Davulcu H. Quantifying Variations in Controversial Discussions within Kuwaiti Social Networks. Big Data and Cognitive Computing 2024;8(6):60 View