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

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
  10. Boby M, Oh H, Marsiglia F, Liu L. Bridging social capital among Facebook users and COVID-19 cases growth in Arizona. Social Science & Medicine 2024;360:117313 View
  11. Yang X, Luo C, Xu Y, He Y, Zhao R. Unpacking cyberchondria: The roles of online health information seeking, health information overload, and health misperceptions. Telematics and Informatics 2024:102225 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