This study investigates students' social networks and mental health before and at the time of the COVID-19 pandemic in April 2020, using longitudinal data collected since 2018. We analyze change on multiple dimensions of social networks (interaction, friendship, social support, co-studying) and mental health indicators (depression, anxiety, stress, loneliness) within two cohorts of Swiss undergraduate students experiencing the crisis (N = 212), and make additional comparisons to an earlier cohort which did not experience the crisis (N = 54). In within-person comparisons we find that interaction and co-studying networks had become sparser, and more students were studying alone. Furthermore, students' levels of stress, anxiety, loneliness, and depressive symptoms got worse, compared to measures before the crisis. Stressors shifted from fears of missing out on social life to worries about health, family, friends, and their future. Exploratory analyses suggest that COVID-19 specific worries, isolation in social networks, lack of interaction and emotional support, and physical isolation were associated with negative mental health trajectories. Female students appeared to have worse mental health trajectories when controlling for different levels of social integration and COVID-19 related stressors. As universities and researchers discuss future strategies on how to combine on-site teaching with online courses, our results indicate the importance of considering social contacts in students' mental health and offer starting points to identify and support students at higher risk of social isolation and negative psychological effects during the COVID-19 pandemic.

SignificanceUnderstanding the factors that explain academic failure and success of university students is a core interest of educational researchers, teachers, and managers. We demonstrate how the dynamic social networks that informally evolve between students can affect their academic performance. We closely followed the emergence of multiple social networks within a cohort of 226 undergraduate university students. They were strangers to each other on their first day at university, but developed densely knit social networks through time. We show that functional studying relationships tended to evolve from informal friendship relations. In a critical examination period after one year, these networks proved to be crucial: Socially isolated students had significantly lower examination grades and were more likely to drop out of university.

Depressive symptoms are associated with social isolation in face-to-face interaction networks timon elmer * & christoph Stadtfeld individuals with depressive symptoms are more likely to be isolated in their social networks, which can further increase their symptoms. Although social interactions are an important aspect of individuals' social lives, little is known about how depressive symptoms affect behavioral patterns in social interaction networks. This article analyzes the effect of depressive symptoms on social interactions in two empirical settings (n total = 123, N dyadic relations = 2,454) of students spending a weekend together in a remote camp house. We measured social interactions between participants with Radio frequency Identification (RFID) nametags. Prior to the weekend, participants were surveyed on their depressive symptoms and friendship ties. Using state-of-the-art social network analysis methods, we test four preregistered hypotheses. Our results indicate that depressive symptoms are associated with (1) spending less time in social interaction, (2) spending time with similarly depressed others, (3) spending time in pair-wise interactions rather than group interactions but not with (4) spending relatively less time with friends. By "zooming in" on face-to-face social interaction networks, these findings offer new insights into the social consequences of depressive symptoms. Social interactions are the smallest building blocks of interpersonal social networks and are a prerequisite of the formation of functional social relationships. The lack of social interactions and social relationships (i.e., social isolation) can have detrimental effects on an individual's physical and psychological health. Social isolation increases the risk for coronary heart disease, stroke, and mortality 1-3 and can negatively influence psychological health leading to depressive symptoms 4,5. But social isolation can also be the consequence of depressive symptoms. It is well established that individuals with depressive symptoms have less rewarding and more dysfunctional social relationships 6-8. In that vein, longitudinal social network studies have shown that depressive symptoms affect the creation, maintenance, and termination of social ties 9,10. While the effects of depressive symptoms have mostly been examined in self-reported friendship networks, many processes are in fact argued to operate on the more fine-grained level of social interactions 9,11-14. Investigating the social processes on an interaction level can help us to understand how depressive symptoms contribute to being socially isolated. This paper thus develops and tests four preregistered hypotheses on how depressive symptoms affect face-to-face interactions in social networks. The first hypothesis (depression-isolation hypothesis) states that depressive symptoms are associated with less social interactions. It has been argued that depressive symptoms are accompanied by a change of social skills and motivation to socialize (e.g., more reassurance seekin...

Individuals' favorite subjects in school can predetermine their educational and occupational careers. If girls develop weaker preferences for science, technology, engineering, and math (STEM), it can contribute to macrolevel gender inequalities in income and status. Relying on large-scale panel data on adolescents from Sweden (218 classrooms, 4,998 students), we observe a widening gender gap in preferring STEM subjects within a year (girls, 19 to 15 percent; boys, 21 to 20 percent). By applying newly developed randomcoefficient multilevel stochastic actor-oriented models on social network data (27,428 friendships), we investigate how social context contributes to those changes. We find strong evidence that students adjust their preferences to those of their friends (friend influence). Moreover, girls tend to retain their STEM preferences when other girls in their classroom also like STEM (peer exposure). We conclude that these mechanisms amplify preexisting preferences and thereby contribute to the observed dramatic widening of the STEM gender gap.

Ample theoretical work on social networks is explicitly or implicitly concerned with the role of interpersonal interaction. However, empirical studies to date mostly focus on the analysis of stable relations. This article introduces Dynamic Network Actor Models (DyNAMs) for the study of directed, interpersonal interaction through time. The presented model addresses three important aspects of interpersonal interaction. First, interactions unfold in a larger social context and depend on complex structures in social systems. Second, interactions emanate from individuals and are based on personal preferences, restricted by the available interaction opportunities. Third, sequences of interactions develop dynamically, and the timing of interactions relative to one another contains useful information. We refer to these aspects as the network nature, the actor-oriented nature, and the dynamic nature of social interaction. A case study compares the DyNAM framework to the relational event model, a widely used statistical method for the study of social interaction data.

Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso-and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.

While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs -an extensions of ERGMs -and process-based models using SAOMs as an example. We conclude that the basic TERGM, in contrast to the ERGM, has no consistent micro-level interpretation, and thus only allows interpretation on the level of the network. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitations is true for processbased network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models.

This study investigates students’ social networks and mental health before and at the time of the COVID-19 pandemic in April 2020 in Switzerland, using longitudinal data collected since 2018. We analyze change on multiple dimensions of social networks(interaction, friendship, social support, co-studying) and mental health indicators (depression, anxiety, stress, loneliness) within two cohorts of Swiss undergraduatestudents experiencing the crisis (N=212), and make additional comparisons to an earlier cohort which did not experience the crisis (N=54). In within-person comparisons we find that interaction and co-studying networks had become sparser, and more students were studying alone. Furthermore, students’ levels of stress, anxiety, loneliness, and depressive symptoms got worse, compared to measures before the crisis. Stressors shifted from fears of missing out on social life to worries about health, family, friends, and their future. Exploratory analyses suggest that COVID-19 specific worries, isolation in social networks, lack of interaction and emotional support, and physical isolation were associated with negative mental health trajectories. Female students appeared to have worse mental health trajectories when controlling for different levels of social integration and COVID-19 related stressors. As universities and researchers discuss future strategies on how to combine on-site teaching with online courses, our results indicate the importance of considering social contacts in students’ mental health and offer starting points to identify and support students at higher risk of social isolation and negative psychological effects during the COVID-19 pandemic.