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

Diabetes Surveillance and Epidemiology (19) Infodemiology and Infoveillance (1272) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1012)

Published on in Vol 5, No 1 (2020): Jan-Mar

Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis

Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis

Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis

Authors of this article:

Heather Griffis1 Author Orcid Image ;   David A Asch2 Author Orcid Image ;   H Andrew Schwartz3 Author Orcid Image ;   Lyle Ungar2 Author Orcid Image ;   Alison M Buttenheim2 Author Orcid Image ;   Frances K Barg2 Author Orcid Image ;   Nandita Mitra2 Author Orcid Image ;   Raina M Merchant2 Author Orcid Image
Article Authors Cited by (9) Tweetations (7) Metrics

Journals

  1. Anwar M, Khoury D, Aldridge A, Parker S, Conway K. Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study. JMIR Public Health and Surveillance 2020;6(2):e17574 View
  2. Xue J, Chen J, Chen C, Zheng C, Li S, Zhu T, Zhao J. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLOS ONE 2020;15(9):e0239441 View
  3. Iglesias‐Puzas Á, Conde‐Taboada A, Aranegui‐Arteaga B, López‐Bran E. Understanding the role of social media to support patients with mastocytosis: Content analysis of Facebook communities. Australasian Journal of Dermatology 2021;62(2) View
  4. Doogan C, Buntine W, Linger H, Brunt S. Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data. Journal of Medical Internet Research 2020;22(9):e21419 View
  5. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
  6. Ricard B, Hassanpour S. Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes. Journal of Medical Internet Research 2021;23(9):e27314 View
  7. Fu J, Li C, Zhou C, Li W, Lai J, Deng S, Zhang Y, Guo Z, Wu Y. Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review. Journal of Medical Internet Research 2023;25:e43349 View
  8. Gu D, Wang Q, Chai Y, Yang X, Zhao W, Li M, Zolotarev O, Xu Z, Zhang G. Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis. Journal of Medical Internet Research 2024;26:e48324 View
  9. Nagpal M, Sherifali D, Morita P, Cafazzo J. Managing Type 2 Diabetes during the COVID-19 Pandemic: Comparison Between Systematic Literature Review and Social Media Data (Preprint). Interactive Journal of Medical Research 2023 View