A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related...

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Detalles Bibliográficos
Autores principales: Klein, Ari Z., Magge, Arjun, O’Connor, Karen M.S., Cai, Haitao, Weissenbacher, Davy, Gonzalez-Hernandez, Graciela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276035/
https://www.ncbi.nlm.nih.gov/pubmed/32511608
http://dx.doi.org/10.1101/2020.04.19.20069948
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author Klein, Ari Z.
Magge, Arjun
O’Connor, Karen M.S.
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
author_facet Klein, Ari Z.
Magge, Arjun
O’Connor, Karen M.S.
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
author_sort Klein, Ari Z.
collection PubMed
description The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.
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spelling pubmed-72760352020-06-07 A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter Klein, Ari Z. Magge, Arjun O’Connor, Karen M.S. Cai, Haitao Weissenbacher, Davy Gonzalez-Hernandez, Graciela medRxiv Article The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19. Cold Spring Harbor Laboratory 2020-04-24 /pmc/articles/PMC7276035/ /pubmed/32511608 http://dx.doi.org/10.1101/2020.04.19.20069948 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/It is made available under a CC-BY-NC-ND 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Article
Klein, Ari Z.
Magge, Arjun
O’Connor, Karen M.S.
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title_full A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title_fullStr A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title_full_unstemmed A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title_short A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
title_sort chronological and geographical analysis of personal reports of covid-19 on twitter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276035/
https://www.ncbi.nlm.nih.gov/pubmed/32511608
http://dx.doi.org/10.1101/2020.04.19.20069948
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