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Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data.
Sullivan, Katherine J; Burden, Marisha; Keniston, Angela; Banda, Juan M; Hunter, Lawrence E.
  • Sullivan KJ; Data Science to Patient Value, University of Colorado School of Medicine, Aurora, CO 80045, USA* Corresponding author, Katherine.Sullivan@CUAnschutz.edu.
Pac Symp Biocomput ; 26: 95-106, 2021.
Article in English | MEDLINE | ID: covidwho-1124173
ABSTRACT
Physicians' beliefs and attitudes about COVID-19 are important to ascertain because of their central role in providing care to patients during the pandemic. Identifying topics and sentiments discussed by physicians and other healthcare workers can lead to identification of gaps relating to theCOVID-19 pandemic response within the healthcare system. To better understand physicians' perspectives on the COVID-19 response, we extracted Twitter data from a specific user group that allows physicians to stay anonymous while expressing their perspectives about the COVID-19 pandemic. All tweets were in English. We measured most frequent bigrams and trigrams, compared sentiment analysis methods, and compared our findings to a larger Twitter dataset containing general COVID-19 related discourse. We found significant differences between the two datasets for specific topical phrases. No statistically significant difference was found in sentiments between the two datasets, and both trended slightly more positive than negative. Upon comparison to manual sentiment analysis, it was determined that these sentiment analysis methods should be improved to accurately capture sentiments of anonymous physician data. Anonymous physician social media data is a unique source of information that provides important insights into COVID-19 perspectives.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Physicians / Social Media / COVID-19 Limits: Humans Language: English Journal: Pac Symp Biocomput Journal subject: Biotechnology / Medical Informatics Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Physicians / Social Media / COVID-19 Limits: Humans Language: English Journal: Pac Symp Biocomput Journal subject: Biotechnology / Medical Informatics Year: 2021 Document Type: Article