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

Infodemiology and Infoveillance (1274) JMIR Theme Issue: COVID-19 Special Issue (2391) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Vaccination and Immunization in the Digital Age (346) Information Quality in Digital Media (318) Infectious Diseases (non-STD/STI) (1485) COVID-19 Vaccination (128) Vaccination Sentiment and Anti-Vaccination Infodemiology (50) Quality/Credibility of eHealth Information and Trust Issues (489) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29127, first published .
Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

Authors of this article:

Iain Cruickshank1 Author Orcid Image ;   Tamar Ginossar2 Author Orcid Image ;   Jason Sulskis3 Author Orcid Image ;   Elena Zheleva3 Author Orcid Image ;   Tanya Berger-Wolf3, 4 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. Lattimer T, Tenzek K, Ophir Y, Sullivan S. Exploring Web-Based Twitter Conversations Surrounding National Healthcare Decisions Day and Advance Care Planning From a Sociocultural Perspective: Computational Mixed Methods Analysis. JMIR Formative Research 2022;6(4):e35795 View
  2. Shaw N, Hakam N, Lui J, Abbasi B, Sudhakar A, Leapman M, Breyer B. COVID-19 Misinformation and Social Network Crowdfunding: Cross-sectional Study of Alternative Treatments and Antivaccine Mandates. Journal of Medical Internet Research 2022;24(7):e38395 View
  3. Béres F, Michaletzky T, Csoma R, Benczúr A. Network embedding aided vaccine skepticism detection. Applied Network Science 2023;8(1) View
  4. Ju W, Sannusi S, Mohamad E. “Public goods” or “diplomatic tools”: a framing research on Chinese and American media reports regarding Chinese COVID-19 vaccine. Media Asia 2023;50(1):43 View
  5. Ginossar T, Cruickshank I, Zheleva E, Sulskis J, Berger-Wolf T. Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations. Human Vaccines & Immunotherapeutics 2022;18(1):1 View
  6. Ng L, Cruickshank I, Carley K. Cross-platform information spread during the January 6th capitol riots. Social Network Analysis and Mining 2022;12(1) View
  7. 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
  8. Zhu J, Weng F, Zhuang M, Lu X, Tan X, Lin S, Zhang R. Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model. International Journal of Environmental Research and Public Health 2022;19(20):13248 View
  9. Kim S, Kim K, Wonjeong Jo C. Accuracy of a large language model in distinguishing anti- and pro-vaccination messages on social media: The case of human papillomavirus vaccination. Preventive Medicine Reports 2024;42:102723 View

Books/Policy Documents

  1. Ginossar T, Shah S, Weiss D. Vaccine Communication Online. View
  2. Blane J, Ng L, Carley K. Vaccine Communication Online. View
  3. Himelboim I, Lee J, Cacciatore M, Kim S, Krause D, Miller-Bains K, Mattson K, Reynolds J. Vaccine Communication Online. View
  4. Hoffman B, Sidani J, Burke J, Chu K, Felter E. Vaccine Communication Online. View