All about that trait: Examining extraversion and state anxiety during the SARS-CoV-2 pandemic using a machine learning approach

Dritjon Gruda; Adegboyega Ojo

doi:10.1016/j.paid.2021.111461

All about that trait: Examining extraversion and state anxiety during the SARS-CoV-2 pandemic using a machine learning approach

Pers Individ Dif. 2022 Apr:188:111461. doi: 10.1016/j.paid.2021.111461. Epub 2021 Dec 21.

Authors

Dritjon Gruda¹, Adegboyega Ojo¹

Affiliation

¹ National University of Ireland Maynooth, Faculty of Social Sciences, Maynooth, Ireland.

Abstract

We examine the longitudinal relation between extraversion and state anxiety in a large cohort of New York City (NYC) residents using a linguistic analytical machine learning approach. Anxiety, both state and trait, and Big Five personality traits were predicted using micro-blog data on the Twitter platform. In total, we examined 1336 individuals and a total of 200,289 observations across 246 days. We find that before the onset of SARS-CoV-2 in NYC (before 1st March 2020), extraverts experienced lower state anxiety compared to introverted individuals, while this difference shrinks after the onset of the pandemic, which provides evidence that SARS-COV-2 is affecting all individuals regardless of their extraversion trait disposition. Secondly, a longitudinal examination of the presented 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. We interpret results within the unique SARS-CoV-2 context and discuss the relationship between SARS-COV-2 and individual differences, namely personality traits. Finally, we discuss results and outline the limitations of our approach.

Keywords: Anxiety; COVID-19; Extraversion; Longitudinal; Machine learning.