SummaryBackgroundThe experience of social exclusion represents an extremely aversive and threatening situation in daily life. The present study examined the impact of social exclusion compared to inclusion on steroid hormone concentrations as well as on subjective affect ratings.MethodsEighty subjects (40 females) participated in two independent behavioral experiments. They engaged in a computerized ball tossing game in which they ostensibly played with two other players who deliberately excluded or included them, respectively. Hormone samples as well as mood ratings were taken before and after the game.ResultsSocial exclusion led to a decrease in positive mood ratings and increased anger ratings. In contrast, social inclusion did not affect positive mood ratings, but decreased sadness ratings. Both conditions did not affect cortisol levels. Testosterone significantly decreased after being excluded in both genders, and increased after inclusion, but only in males. Interestingly, progesterone showed an increase after both conditions only in females.DiscussionOur results suggest that social exclusion does not trigger a classical stress response but gender-specific changes in sex hormone levels. The testosterone decrease after being excluded in both genders, as well as the increase after inclusion in males can be interpreted within the framework of the biosocial status hypothesis. The progesterone increase might reflect a generalized affiliative response during social interaction in females.

Background There is accumulating evidence about detrimental impacts of the pandemic on population mental health, but knowledge on risk of groups specifically affected by the pandemic and variations across time is still limited. Methods We surveyed approximately n =1,000 Austrian residents in 12 waves between April and December 2020 ( n =12,029). Outcomes were suicidality (Beck Suicidal Ideation Scale), depressive symptoms (Patient Health Questionnaire-9), anxiety (Hospital Anxiety Depression Scale), and domestic violence. We also assessed the perceived burden from the pandemic. Demographic and Covid-19 specific occupational and morbidity-related variables were used to explain outcomes in multivariable regression analyses, controlling for well-established risk factors of mental ill-health, and variations over time were analyzed. Results Young age, working in healthcare or from home, and own Covid-19 illness were consistent risk factors controlling for a wide range of known mental health risk factors. Time patterns in the perceived burden from Covid-19-related measures were consistent with the time sequence of restrictions and relaxations of governmental measures. Depression and anxiety were relatively stable over time, with some increase of depression during the second phase of lockdowns. Domestic violence increased immediately after both hard lockdowns. Suicidal ideation decreased slightly over time, with a low during the second hard lockdown. Mental health indicators for women and young people showed some deterioration over time, whereas those reporting own Covid-19 illness improved. Limitations Data from before the pandemic were not available. Conclusions Among mental health outcomes, increases in domestic violence and, to some smaller extent, depressive symptoms, appeared most closely related to the timing of hard lockdowns. Healthcare staff, individuals working from home, those with Covid-19, as well as young people and women are non-traditional risk groups who warrant heightened attention in prevention during and in the aftermath of the pandemic. Keywords: Covid-19, pandemic, mental health, survey, Austria

To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This allows decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We used web scraping and API access to retrieve data from the news platform derstandard.at, Twitter, and a chat platform for students. We documented the technical details of our workflow to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allowed us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We used special word clouds to visualize that overall difference. Longitudinally, our time series showed spikes in anxiety that can be linked to several events and media reporting. Additionally, we found a marked decrease in anger. The changes lasted for remarkably long periods of time (up to 12 weeks). We have also discussed these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data