Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK

The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language pr...

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Detalles Bibliográficos
Autores principales: Golder, S, Klein, Ari Z., Magge, Arjun, O’Connor, Karen, 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/PMC7273260/
https://www.ncbi.nlm.nih.gov/pubmed/32511492
http://dx.doi.org/10.1101/2020.05.05.20083436
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author Golder, S
Klein, Ari Z.
Magge, Arjun
O’Connor, Karen
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
author_facet Golder, S
Klein, Ari Z.
Magge, Arjun
O’Connor, Karen
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
author_sort Golder, S
collection PubMed
description The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.
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spelling pubmed-72732602020-06-07 Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK Golder, S Klein, Ari Z. Magge, Arjun O’Connor, Karen Cai, Haitao Weissenbacher, Davy Gonzalez-Hernandez, Graciela medRxiv Article The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England. Cold Spring Harbor Laboratory 2020-05-08 /pmc/articles/PMC7273260/ /pubmed/32511492 http://dx.doi.org/10.1101/2020.05.05.20083436 Text en http://creativecommons.org/licenses/by-nd/4.0/It is made available under a CC-BY-ND 4.0 International license (http://creativecommons.org/licenses/by-nd/4.0/) .
spellingShingle Article
Golder, S
Klein, Ari Z.
Magge, Arjun
O’Connor, Karen
Cai, Haitao
Weissenbacher, Davy
Gonzalez-Hernandez, Graciela
Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title_full Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title_fullStr Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title_full_unstemmed Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title_short Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
title_sort extending a chronological and geographical analysis of personal reports of covid-19 on twitter to england, uk
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273260/
https://www.ncbi.nlm.nih.gov/pubmed/32511492
http://dx.doi.org/10.1101/2020.05.05.20083436
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