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

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19276, first published .
Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

Authors of this article:

Abdullah Wahbeh1 Author Orcid Image ;   Tareq Nasralah2 Author Orcid Image ;   Mohammad Al-Ramahi3 Author Orcid Image ;   Omar El-Gayar4 Author Orcid Image
Article Authors Cited by (58) Tweetations (2) Metrics

Journals

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  20. Dogan O, Tiwari S, Jabbar M, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. Complex & Intelligent Systems 2021;7(5):2655 View
  21. Wu J, Sivaraman V, Kumar D, Banda J, Sontag D. Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media. Journal of Biomedical Informatics 2021;120:103844 View
  22. Chen Q, Leaman R, Allot A, Luo L, Wei C, Yan S, Lu Z. Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing. Annual Review of Biomedical Data Science 2021;4(1):313 View
  23. Elyashar A, Plochotnikov I, Cohen I, Puzis R, Cohen O. The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses. Journal of Medical Internet Research 2021;23(10):e30217 View
  24. Choi Y, Kim S. Public library Twitter use during the early period of the COVID-19 lockdown in the United States. The Electronic Library 2021;39(5):713 View
  25. Troisi O, Fenza G, Grimaldi M, Loia F. Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic. Computers in Human Behavior 2022;126:106986 View
  26. Abdel-Razig S, Anglade P, Ibrahim H. Impact of the COVID-19 Pandemic on a Physician Group’s WhatsApp Chat: Qualitative Content Analysis. JMIR Formative Research 2021;5(12):e31791 View
  27. DePaula N, Hagen L, Roytman S, Alnahass D. Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook. JMIR Infodemiology 2022;2(2):e40198 View
  28. Lin S, Yeh W, Chiu Y, Chang Y, Hsu M, Chen Y, Hsu W. A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. Database 2022;2022 View
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  30. Xu W, Tshimula J, Dubé È, Graham J, Greyson D, MacDonald N, Meyer S. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach. JMIR Infodemiology 2022;2(2):e41198 View
  31. Xavier T, Lambert J. Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic. Journal of Nursing Scholarship 2022;54(5):613 View
  32. Septia Irawan A, Shahin B, Wangeshi Njuguna D, Nellamkuzhi N, Thiện B, Mahrouseh N, Varga O. Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter. Frontiers in Nutrition 2022;9 View
  33. Khalifé M, Chaker R, Gasparovic S. Environmental education and digital solutions: An analysis of the Lebanese context’s existing and possible digital actions. Frontiers in Education 2022;7 View
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Books/Policy Documents

  1. Wright C, Knopp M, Knopp M. Molecular Imaging. View