Gruda Dritjon, Ojo Adegboyega
National University of Ireland Maynooth, Faculty of Social Sciences, Maynooth, Ireland.
Pers Individ Dif. 2022 Apr;188:111461. doi: 10.1016/j.paid.2021.111461. Epub 2021 Dec 21.
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.
我们使用语言分析机器学习方法，在一大群纽约市居民中研究外向性与状态焦虑之间的纵向关系。利用推特平台上的微博数据预测状态焦虑和特质焦虑以及大五人格特质。我们总共研究了1336个人，在246天内共有200289条观测数据。我们发现，在纽约市出现SARS-CoV-2之前（2020年3月1日之前），外向者比内向者经历的状态焦虑更低，而在疫情爆发后这种差异缩小，这表明SARS-CoV-2正在影响所有个体，无论其外向性特质倾向如何。其次，对所呈现数据的纵向研究表明，外向性在危机初期和我们研究时间段接近尾声时似乎影响更大。我们在独特的SARS-CoV-2背景下解读结果，并讨论SARS-CoV-2与个体差异（即人格特质）之间的关系。最后，我们讨论结果并概述我们方法的局限性。
