Tweet you right back: Follower anxiety predicts leader anxiety in social media interactions during the SARS-CoV-2 pandemic.

Recent research has shown that organizational leaders' tweets can influence employee anxiety. In this study, we turn the table and examine whether the same can be said about followers' tweets. Based on emotional contagion and a dataset of 108 leaders and 178 followers across 50 organizations, we infer and track state- and trait-anxiety scores of participants over 316 days, including pre- and post the onset of the SARS-CoV-2 pandemic and crisis. We show that although leaders traditionally possess greater authority and power than their followers, followers have the power to influence their leaders' state anxiety. In addition, this influence is particularly strong in the case of less trait anxious leaders.

Don't you tweet me badly: Anxiety contagion between leaders and followers in computer-mediated communication during COVID-19.

Do organizational leaders' tweets influence their employees' anxiety? And if so, have employees become more susceptible to their leader's social media communications during the COVID-19 pandemic? Based on emotional contagion and using machine learning algorithms to track anxiety and personality traits of 197 leaders and 958 followers across 79 organizations over 316 days, we find that during the pandemic leaders' tweets do influence follower state anxiety. In addition, followers of trait anxious leaders seem somewhat protected by sudden spikes in leader state anxiety, while followers of less trait anxious leaders are most affected by increased leader state anxiety. Multi-day lagged regressions showcase that this effect is stronger post-onset of the COVID-19 pandemic compared to the pre-pandemic crisis context.

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

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.

I Need a Doctor, Call Me a Doctor: Attachment and the Evaluation of General Practitioners before and during the COVID-19 Pandemic.

Attachment is a system of threat regulation, and insecure (anxious and avoidant) attachment orientations are important individual difference antecedents to the cognitive and affective attributions of trait inferences. However, little is known about how threat-related contexts, such as the current COVID-19 pandemic, influence attachment-related socio-cognitive schemas. Using an experimental research design across two independent samples of 330 (pre-onset of COVID-19) and 233 (post-onset of COVID-19) participants, we tested whether attachment orientations influenced general practitioner (GP) ratings and selection differently pre- and post-onset of the COVID-19 pandemic. We found that during the COVID-19 pandemic, when presented with only negative information signals, avoidant individuals attributed positive ratings to GPs, with differing ratings as the number of positive signals increased. Differences between pre- and post-onset of the COVID-19 pandemic were less pronounced with regards to positive signals. We discuss these results in line with signal detection theory (SDT) and provide practical implications in response to our findings.