151
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

College student expression on Twitter during the COVID-19 pandemic

, MS, , PhDORCID Icon, , PhDORCID Icon, , PhD & , PhDORCID Icon
Pages 722-730 | Received 31 Mar 2021, Accepted 13 Mar 2022, Published online: 15 Apr 2022
 

Abstract

Objective:The current study longitudinally examines college student Twitter patterns throughout initial phases of the COVID-19 pandemic. This study aims to better understand psychological impact and online personal communication during the pandemic.

Participants:A dataset consisting of ∼720,000 tweets posted by students from universities throughout the United States during the 2020 spring semester was analyzed according to structural and sentimental analysis.

Methods:Using a data-driven approach, three time periods emerged which reflected the transition to online learning.

Results:Significant changes in structure and sentiment of tweets were observed across phases.

Conclusions:Changes in Twitter patterns revealed important features of this unprecedented transition to online learning for college students.

Conflict of interest disclosure

The authors report there are no competing interests to declare. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements of the United States of America.

Additional information

Funding

No funding was used to support this research and/or the preparation of the manuscript.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 141.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.