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

Vaccination Sentiment and Anti-Vaccination Infodemiology (62) JMIR Theme Issue: COVID-19 Special Issue (2461) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Outbreak and Pandemic Preparedness and Management (1743) Infectious Diseases (non-STD/STI) (1594) Infodemiology and Infoveillance (1372) Natural Language Processing (709) Vaccination and Immunization in the Digital Age (379) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1051)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32372, first published .
Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis

Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis

Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis

Authors of this article:

Eden Engel-Rebitzer1, 2 Author Orcid Image ;   Daniel C Stokes1, 2 Author Orcid Image ;   Zachary F Meisel2, 3, 4, 5 Author Orcid Image ;   Jonathan Purtle6, 7 Author Orcid Image ;   Rebecca Doyle3, 8 Author Orcid Image ;   Alison M Buttenheim3, 8 Author Orcid Image
Article Authors Cited by (6) Tweetations (18) Metrics Author Video

Journals

  1. Choi Y, Fox A. Mistrust in public health institutions is a stronger predictor of vaccine hesitancy and uptake than Trust in Trump. Social Science & Medicine 2022;314:115440 View
  2. Cascini F, Pantovic A, Al-Ajlouni Y, Failla G, Puleo V, Melnyk A, Lontano A, Ricciardi W. Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature. eClinicalMedicine 2022;48:101454 View
  3. Scott J, Collier K, Pugel J, O’Neill P, Long E, Fernandes M, Cruz K, Gay B, Giray C, Crowley D. SciComm Optimizer for Policy Engagement: a randomized controlled trial of the SCOPE model on state legislators’ research use in public discourse. Implementation Science 2023;18(1) View
  4. Relihan D, Holman E, Garfin D, Ditto P, Silver R. Politicization of a Pathogen: A Prospective Longitudinal Study of COVID‐19 Responses in a Nationally Representative U.S. Sample. Political Psychology 2023;44(6):1193 View
  5. White B, Gombert A, Nguyen T, Yau B, Ishizumi A, Kirchner L, León A, Wilson H, Jaramillo-Gutierrez G, Cerquides J, D’Agostino M, Salvi C, Sreenath R, Rambaud K, Samhouri D, Briand S, Purnat T. Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study. JMIR Infodemiology 2023;3:e47317 View
  6. Dickson Z, Yildirim T. The Effects of COVID-19 Infection on Opposition to COVID-19 Policies: Evidence from the U.S. Congress. Political Communication 2024:1 View