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](https://asset.jmir.pub/assets/e637d55e815d5e309ca94c334de4fa07.png 480w,https://asset.jmir.pub/assets/e637d55e815d5e309ca94c334de4fa07.png 960w,https://asset.jmir.pub/assets/e637d55e815d5e309ca94c334de4fa07.png 1920w,https://asset.jmir.pub/assets/e637d55e815d5e309ca94c334de4fa07.png 2500w)
Journals
- Sigalo N, Frias-Martinez V. Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study. JMIR Infodemiology 2023;3:e43700 View
- Zhang J, Wang Y, Mouton M, Zhang J, Shi M. Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis. Journal of Medical Internet Research 2024;26:e53375 View
- Sasse K, Mahabir R, Gkountouna O, Crooks A, Croitoru A, Koh K. Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media. PLOS ONE 2024;19(6):e0301488 View