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

JMIR Theme Issue: COVID-19 Special Issue (2391) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Outbreak and Pandemic Preparedness and Management (1675) Infectious Diseases (non-STD/STI) (1485) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013) Infodemiology and Infoveillance (1274)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27300, first published .
Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study

Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study

Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study

Authors of this article:

Sharath Chandra Guntuku1, 2, 3 Author Orcid Image ;   Jonathan Purtle4 Author Orcid Image ;   Zachary F Meisel3, 5, 6 Author Orcid Image ;   Raina M Merchant2, 3, 5, 6 Author Orcid Image ;   Anish Agarwal2, 3, 5, 6 Author Orcid Image
Article Authors Cited by (10) Tweetations (16) Metrics

Journals

  1. Hu T, Wang S, Luo W, Zhang M, Huang X, Yan Y, Liu R, Ly K, Kacker V, She B, Li Z. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. Journal of Medical Internet Research 2021;23(9):e30854 View
  2. Engel-Rebitzer E, Stokes D, Meisel Z, Purtle J, Doyle R, Buttenheim A. Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis. JMIR Infodemiology 2022;2(1):e32372 View
  3. Satherley N, Zubielevitch E, Greaves L, Barlow F, Osborne D, Sibley C. Political attitude change over time following COVID-19 lockdown: Rallying effects and differences between left and right voters. Frontiers in Psychology 2022;13 View
  4. Németh R. A scoping review on the use of natural language processing in research on political polarization: trends and research prospects. Journal of Computational Social Science 2023;6(1):289 View
  5. Zhou Y, Myrick J, Farrell E, Cohen O. Perceived risk, emotions, and stress in response to COVID‐19: The interplay of media use and partisanship. Risk Analysis 2023;43(8):1572 View
  6. Purtle J, Nelson K, Gebrekristos L, Lê-Scherban F, Gollust S. Partisan differences in the effects of economic evidence and local data on legislator engagement with dissemination materials about behavioral health: a dissemination trial. Implementation Science 2022;17(1) View
  7. Ranney M, Friedhoff S. Public communication about public health where we really need to go. npj Digital Medicine 2022;5(1) View
  8. Trevino J, Malik S, Schmidt M. Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study. JMIR Infodemiology 2022;2(1):e32386 View
  9. Abrams M, Pelullo A, Meisel Z, Merchant R, Purtle J, Agarwal A. State and Federal Legislators’ Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis. JMIR Infodemiology 2023;3:e38676 View
  10. 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