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Trump’s COVID-19 tweets and Dr. Fauci’s emails

The paper features an analysis of former President Trump’s early tweets on COVID-19 in the context of Dr. Fauci’s recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentimen...

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Detalles Bibliográficos
Autores principales: Allen, David E., McAleer, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761249/
https://www.ncbi.nlm.nih.gov/pubmed/35068618
http://dx.doi.org/10.1007/s11192-021-04243-z
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author Allen, David E.
McAleer, Michael
author_facet Allen, David E.
McAleer, Michael
author_sort Allen, David E.
collection PubMed
description The paper features an analysis of former President Trump’s early tweets on COVID-19 in the context of Dr. Fauci’s recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot’s power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act.
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spelling pubmed-87612492022-01-18 Trump’s COVID-19 tweets and Dr. Fauci’s emails Allen, David E. McAleer, Michael Scientometrics Article The paper features an analysis of former President Trump’s early tweets on COVID-19 in the context of Dr. Fauci’s recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot’s power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act. Springer International Publishing 2022-01-16 2022 /pmc/articles/PMC8761249/ /pubmed/35068618 http://dx.doi.org/10.1007/s11192-021-04243-z Text en © Akadémiai Kiadó, Budapest, Hungary 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Allen, David E.
McAleer, Michael
Trump’s COVID-19 tweets and Dr. Fauci’s emails
title Trump’s COVID-19 tweets and Dr. Fauci’s emails
title_full Trump’s COVID-19 tweets and Dr. Fauci’s emails
title_fullStr Trump’s COVID-19 tweets and Dr. Fauci’s emails
title_full_unstemmed Trump’s COVID-19 tweets and Dr. Fauci’s emails
title_short Trump’s COVID-19 tweets and Dr. Fauci’s emails
title_sort trump’s covid-19 tweets and dr. fauci’s emails
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761249/
https://www.ncbi.nlm.nih.gov/pubmed/35068618
http://dx.doi.org/10.1007/s11192-021-04243-z
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