Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70942

Inferring the Relationship between Anxiety and Extraversion from Tweets during COVID19 – A Linguistic Analytics Approach

File Size Format  
0263.pdf 884.57 kB Adobe PDF View/Open

Item Summary

Title:Inferring the Relationship between Anxiety and Extraversion from Tweets during COVID19 – A Linguistic Analytics Approach
Authors:Gruda, Dritjon
Ojo, Adegboyega
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
anxiety
covid-19
extraversion
linguistic analysis
show 1 moremachine learning
show less
Date Issued:05 Jan 2021
Abstract:We investigate the longitudinal relationship between extraversion and experienced state anxiety in a cohort of Twitter users in New York using a linguistic analytics approach. We find that before COVID-19 was declared a pandemic, highly extraverted individuals experienced lower state anxiety compared to more introverted individuals. This is in line with previous literature. However, there seem to be no significant differences between individuals after the pandemic announcement, which provides evidence that COVID-19 is affecting individuals regardless of their extraversion trait disposition. Finally, a longitudinal examination of the present data shows that extraversion seems to matter more greatly in the early days of the crisis and towards the end of our examined time range. Throughout the crisis, state anxiety did not seem to vary much between individuals with different extraversion dispositions.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70942
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.328
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons