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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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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 |
_version_ | 1784633488258891776 |
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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. |
format | Online Article Text |
id | pubmed-8761249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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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