Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42985, first published .
Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Journals

  1. Qorib M, Oladunni T, Denis M, Ososanya E, Cotae P. COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets. International Journal of Environmental Research and Public Health 2023;20(10):5803 View
  2. Holm R, Pocock G, Severson M, Huber V, Smith T, McFadden L. Using wastewater to overcome health disparities among rural residents. Geoforum 2023;144:103816 View
  3. Gu D, Liu H, Zhao H, Yang X, Li M, Liang C. A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demand. Journal of the Association for Information Science and Technology 2024;75(2):152 View
  4. Anderson L, Ness H, Holm R, Smith T. Wastewater-Informed Digital Advertising as a COVID-19 Geotargeted Neighborhood Intervention: Jefferson County, Kentucky, 2021–2022. American Journal of Public Health 2024;114(1):34 View
  5. Deng Z, Ma R, Wu M, Evans R. Netizens' concerns during COVID-19: a topic evolution analysis of Chinese social media platforms. Kybernetes 2023 View