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Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic
Objective The novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19-related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This stud...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cureus
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007019/ https://www.ncbi.nlm.nih.gov/pubmed/33815994 http://dx.doi.org/10.7759/cureus.13594 |
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author | Prager, Ross Pratte, Michael T Unni, Rudy R Bala, Sudarshan Ng Fat Hing, Nicholas Wu, Kay McGrath, Trevor A Thomas, Adam Thoma, Brent Kyeremanteng, Kwadwo |
author_facet | Prager, Ross Pratte, Michael T Unni, Rudy R Bala, Sudarshan Ng Fat Hing, Nicholas Wu, Kay McGrath, Trevor A Thomas, Adam Thoma, Brent Kyeremanteng, Kwadwo |
author_sort | Prager, Ross |
collection | PubMed |
description | Objective The novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19-related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This study provides a detailed content and contributor analysis of Covid-19-related tweets among the FOAM community during the first months of the pandemic. Design, Setting, and Participants In this social media content analysis study, Twitter was searched from November 1, 2019, to March 21, 2020, for English tweets discussing Covid-19 in the FOAM community. Tweets were classified into one of 13 pre-specified content categories: original research, editorials, FOAM resource, public health, podcast or video, learned experience, refuting false information, policy discussion, emotional impact, blatantly false information, other Covid-19, and non-Covid-19. Further analysis of linked original research and FOAM resources was performed. One-thousand (1000) randomly selected contributor profiles and those deemed to have contributed false information were analyzed. Results The search yielded 8541 original tweets from 4104 contributors. The number of tweets in each content category were: 1557 other Covid-19 (18.2%), 1190 emotional impact (13.9%), 1122 FOAM resources (13.1%), 1111 policy discussion (13.0%), 928 advice (10.9%), 873 learned experience (10.2%), 424 non-Covid-19 (5.0%), 410 podcast or video (4.8%), 304 editorials (3.6%), 275 original research (3.2%), 245 public health (2.9%), 83 refuting false information (1.0%), and 19 blatantly false (0.2%). Conclusions Early in the Covid-19 pandemic, the FOAM community used Twitter to share Covid-19 learned experiences, online resources, crowd-sourced advice, and research and to discuss the emotional impact of Covid-19. Twitter also provided a forum for post-publication peer review of new research. Sharing blatantly false information within this community was infrequent. This study highlights several potential benefits from engaging with the FOAM community on Twitter. |
format | Online Article Text |
id | pubmed-8007019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cureus |
record_format | MEDLINE/PubMed |
spelling | pubmed-80070192021-04-01 Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic Prager, Ross Pratte, Michael T Unni, Rudy R Bala, Sudarshan Ng Fat Hing, Nicholas Wu, Kay McGrath, Trevor A Thomas, Adam Thoma, Brent Kyeremanteng, Kwadwo Cureus Emergency Medicine Objective The novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19-related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This study provides a detailed content and contributor analysis of Covid-19-related tweets among the FOAM community during the first months of the pandemic. Design, Setting, and Participants In this social media content analysis study, Twitter was searched from November 1, 2019, to March 21, 2020, for English tweets discussing Covid-19 in the FOAM community. Tweets were classified into one of 13 pre-specified content categories: original research, editorials, FOAM resource, public health, podcast or video, learned experience, refuting false information, policy discussion, emotional impact, blatantly false information, other Covid-19, and non-Covid-19. Further analysis of linked original research and FOAM resources was performed. One-thousand (1000) randomly selected contributor profiles and those deemed to have contributed false information were analyzed. Results The search yielded 8541 original tweets from 4104 contributors. The number of tweets in each content category were: 1557 other Covid-19 (18.2%), 1190 emotional impact (13.9%), 1122 FOAM resources (13.1%), 1111 policy discussion (13.0%), 928 advice (10.9%), 873 learned experience (10.2%), 424 non-Covid-19 (5.0%), 410 podcast or video (4.8%), 304 editorials (3.6%), 275 original research (3.2%), 245 public health (2.9%), 83 refuting false information (1.0%), and 19 blatantly false (0.2%). Conclusions Early in the Covid-19 pandemic, the FOAM community used Twitter to share Covid-19 learned experiences, online resources, crowd-sourced advice, and research and to discuss the emotional impact of Covid-19. Twitter also provided a forum for post-publication peer review of new research. Sharing blatantly false information within this community was infrequent. This study highlights several potential benefits from engaging with the FOAM community on Twitter. Cureus 2021-02-27 /pmc/articles/PMC8007019/ /pubmed/33815994 http://dx.doi.org/10.7759/cureus.13594 Text en Copyright © 2021, Prager et al. http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Emergency Medicine Prager, Ross Pratte, Michael T Unni, Rudy R Bala, Sudarshan Ng Fat Hing, Nicholas Wu, Kay McGrath, Trevor A Thomas, Adam Thoma, Brent Kyeremanteng, Kwadwo Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title | Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title_full | Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title_fullStr | Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title_full_unstemmed | Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title_short | Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic |
title_sort | content analysis and characterization of medical tweets during the early covid-19 pandemic |
topic | Emergency Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007019/ https://www.ncbi.nlm.nih.gov/pubmed/33815994 http://dx.doi.org/10.7759/cureus.13594 |
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