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

被引：2

作者：

Allen, David E. ^{[1
,2
,3
]}

McAleer, Michael ^{[2
,4
,5
,6
,7
]}

机构：

[1] Univ Sydney, Sch Math & Stat, Sydney, NSW, Australia

[2] Asia Univ, Dept Finance, Coll Management, Taichung, Taiwan

[3] Edith Cowan Univ, Sch Business & Law, Joondalup, Australia

[4] Asia Univ, Dept Bioinformat & Med Engn, Coll Informat & Elect Engn, Taichung, Taiwan

[5] Univ Sydney, Discipline Business Analyt, Business Sch, Sydney, NSW, Australia

[6] Erasmus Univ, Inst Econometr, Erasmus Sch Econ, Rotterdam, Netherlands

[7] Univ Complutense Madrid, Dept Econ Anal & ICAE, Madrid, Spain

来源：

SCIENTOMETRICS | 2022年 / 127卷 / 03期

基金：

澳大利亚研究理事会; 日本学术振兴会;

关键词：

Trump; Tweets; Text mining; Sentiment analysis; Word cloud; COVID; 19; Stock market; Dr Fauci emails; FAKE NEWS; SENTIMENT; FINANCE;

D O I：

10.1007/s11192-021-04243-z

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1643 / 1655

页数：13

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