Published on in Vol 24, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40160, first published .
Using Natural Language Processing to Explore “Dry January” Posts on Twitter: Longitudinal Infodemiology Study

Using Natural Language Processing to Explore “Dry January” Posts on Twitter: Longitudinal Infodemiology Study

Using Natural Language Processing to Explore “Dry January” Posts on Twitter: Longitudinal Infodemiology Study

Journals

  1. Russell A, Montemayor B, Chiang S, Milaham P, Barry A, Lin H, Bergman B, Massey P. Characterizing Twitter chatter about temporary alcohol abstinence during “Dry January”. Alcohol and Alcoholism 2023;58(6):589 View
  2. Valdez D, Soto-Vásquez A, Montenegro M. Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets. Social Science & Medicine 2023;339:116365 View
  3. Edinger A, Valdez D, Walsh-Buhi E, Trueblood J, Lorenzo-Luaces L, Rutter L, Bollen J. Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning. Journal of Medical Internet Research 2023;25:e43841 View
  4. Zhang Q, Huang H, Li J, Niu Y, Sun P, Cheng F. Knowledge, attitudes and practices of patients with chronic pharyngitis toward laryngopharyngeal reflux in Suzhou, China. BMC Public Health 2023;23(1) View
  5. Colditz J, Hsiao L, Bergman B, Best D, Hulsey E, Sidani J, Rollman B, Kraemer K. Characteristics and engagement among English-language online forums for addiction recovery available in the US. Internet Interventions 2024;35:100708 View