Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37142, first published .
Exploring COVID-19–Related Stressors: Topic Modeling Study

Exploring COVID-19–Related Stressors: Topic Modeling Study

Exploring COVID-19–Related Stressors: Topic Modeling Study

Journals

  1. Roh M, Choi Y, Park H. Analysis of Issues in Fitness Centers through News Articles before and after the COVID-19 Pandemic in South Korea: Applying Big Data Analysis. Sustainability 2023;15(3):2660 View
  2. Yao L, Ferawati K, Liew K, Wakamiya S, Aramaki E. Disruptions in the Cystic Fibrosis Community’s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments. Journal of Medical Internet Research 2023;25:e45249 View
  3. Golos A, Guntuku S, Piltch-Loeb R, Leininger L, Simanek A, Kumar A, Albrecht S, Dowd J, Jones M, Buttenheim A, Taskin N. Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign. PLOS ONE 2023;18(3):e0281773 View
  4. Smail B, Aliane H, Abdeldjalil O. Using an explicit query and a topic model for scientific article recommendation. Education and Information Technologies 2023;28(12):15657 View
  5. 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
  6. King J, McQuaid A, Leeson V, Tella O, Crawford M. Characterising subgroups of people with severe COVID anxiety by latent profile analysis. Journal of Affective Disorders 2024;344:115 View
  7. Ruocco L, Zhuang Y, Ng R, Munthali R, Hudec K, Wang A, Vereschagin M, Vigo D. A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health. JAMIA Open 2024;7(1) View