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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Infodemiology and Infoveillance (1369)

Published on in Vol 4, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10834, first published .
Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis

Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis

Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis

Authors of this article:

Christopher Tufts1 Author Orcid Image ;   Daniel Polsky2, 3, 4 Author Orcid Image ;   Kevin G Volpp2, 5, 6 Author Orcid Image ;   Peter W Groeneveld2, 6, 7 Author Orcid Image ;   Lyle Ungar1, 8 Author Orcid Image ;   Raina M Merchant1, 9 Author Orcid Image ;   Arthur P Pelullo1 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

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  12. Alvarez-Mon M, Donat-Vargas C, Santoma-Vilaclara J, Anta L, Goena J, Sanchez-Bayona R, Mora F, Ortega M, Lahera G, Rodriguez-Jimenez R, Quintero J, Álvarez-Mon M. Assessment of Antipsychotic Medications on Social Media: Machine Learning Study. Frontiers in Psychiatry 2021;12 View
  13. Reuter K, Angyan P, Le N, Buchanan T. Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study. JMIR Formative Research 2021;5(11):e29958 View
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