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

Vaccination Sentiment and Anti-Vaccination Infodemiology (58) JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Influenza and Influenza-Like-Illness (ILI) Surveillance (217) Artificial Intelligence (1371) COVID-19 Vaccination (146)

Published on in Vol 1, No 1 (2021): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31983, first published .
Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Authors of this article:

Arriel Benis1, 2 Author Orcid Image ;   Anat Chatsubi1 Author Orcid Image ;   Eugene Levner3 Author Orcid Image ;   Shai Ashkenazi4 Author Orcid Image
Article Authors Cited by (11) Tweetations (18) Metrics

Journals

  1. 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
  2. Aljedaani W, Saad E, Rustam F, de la Torre Díez I, Ashraf I. Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends. Mathematics 2022;10(17):3199 View
  3. Benis A, Banker M, Pinkasovich D, Kirin M, Yoshai B, Benchoam-Ravid R, Ashkenazi S, Seidmann A. Reasons for Utilizing Telemedicine during and after the COVID-19 Pandemic: An Internet-Based International Study. Journal of Clinical Medicine 2021;10(23):5519 View
  4. Yin J. Media Data and Vaccine Hesitancy: Scoping Review. JMIR Infodemiology 2022;2(2):e37300 View
  5. 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
  6. Stevens H, Rasul M, Oh Y. Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights. JMIR Infodemiology 2022;2(2):e37635 View
  7. Kinanti T, Suyono S. Fenomena Speak Up pada Media Twitter (Study Deskriptif Korban Penipuan Melalui Gerakan “A Thread”). Jurnal Bisnis dan Komunikasi Digital 2023;1(1):12 View
  8. Hinson J, Zhao X, Klein E, Badaki‐Makun O, Rothman R, Copenhaver M, Smith A, Fenstermacher K, Toerper M, Pekosz A, Levin S. Multisite development and validation of machine learning models to predict severe outcomes and guide decision‐making for emergency department patients with influenza. Journal of the American College of Emergency Physicians Open 2024;5(2) View

Books/Policy Documents

  1. Given L, Case D, Willson R. Looking for Information. View
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  3. Padilla Cruz M. Evaluating Identities Online. View