Trump's COVID-19 tweets and Dr. Fauci's emails
| Autor: | David E. Allen, Michael McAleer |
|---|---|
| Přispěvatelé: | Econometrics |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Tweets
SDG 16 - Peace Text mining Stock market C19 SDG 16 - Peace Justice and Strong Institutions General Social Sciences COVID-19 Dr Fauci emails Library and Information Sciences Justice and Strong Institutions Article Computer Science Applications Coronavirus Sentiment analysis Trump D79 Word cloud COVID 19 C65 |
| Zdroj: | Scientometrics, 127(3), 1643-1655. Springer Netherlands Scientometrics |
| ISSN: | 0138-9130 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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