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

Gruda, Dritjon
Ojo, Adegboyega
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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.
Data Analytics, Data Mining and Machine Learning for Social Media, anxiety, covid-19, extraversion, linguistic analysis, machine learning
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