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) Infodemiology and Infoveillance (1349) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Public (e)Health, Digital Epidemiology and Public Health Informatics (725) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641)

Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43703, first published .
Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets

Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets

Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets

Authors of this article:

Nekabari Sigalo1 Author Orcid Image ;   Naman Awasthi2 Author Orcid Image ;   Saad Mohammad Abrar2 Author Orcid Image ;   Vanessa Frias-Martinez1 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

Nekabari Sigalo   1 , BSc, MPH, PhD ;   Naman Awasthi   2 , MSc ;   Saad Mohammad Abrar   2 , MSc ;   Vanessa Frias-Martinez   1 , BSc, MSc, PhD

1 College of Information Studies, University of Maryland, College Park, MD, United States

2 Department of Computer Science, University of Maryland, College Park, MD, United States

Corresponding Author:

  • Nekabari Sigalo, BSc, MPH, PhD
  • College of Information Studies
  • University of Maryland
  • 4130 Campus Dr
  • College Park, MD, 20742-4345
  • United States
  • Phone: 1 301-405-2033
  • Email: nsigalo@terpmail.umd.edu