Emotions as infectious diseases in a large social network: the SISa model

Tensor product algorithms for inference of contact network from epidemiological data

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

Emotional contagion in a collective ritual

Collective gatherings are often associated with the alignment of psychophysiological states between members of a crowd. While the process of emotional contagion has been studied extensively in dyads as well as at the population level, our understanding of its operation and dynamics as they unfold in real time in real-world group contexts remains limited. Employing a naturalistic design, we investigated emotional contagion in a public religious ritual by examining the relationship between interpersonal distance and autonomic arousal. We found that proximity in space was associated with heightened affective synchrony between participants in the context of the emotionally laden ritual (a Hindu procession) compared with an unstructured walk along the same route performed by the same group. Our findings contribute to the understanding of collective emotions and their underlying psychophysiological mechanisms, emphasizing the role of cultural practices in shaping collective emotional experiences.

What Makes Groups Emotional?

When people experience emotions in a group, their emotions tend to have stronger intensity and to last longer. Why is that? This question has occupied thinkers throughout history, and with the use of digital media it is even more pressing today. Historically, attention has mainly focused on processes driven by the way emotions are shared between people via emotional interactions. Although interactions are a major driver of group emotionality, I review empirical findings that suggest that understanding group emotionality requires a broader view that integrates two additional processes: how emotions unfold within the social infrastructure in which they are shared and how these processes are affected by people's cognition about emotions. I propose to summarize the literature using an infrastructure-cognition-interaction framework that contributes to a broader understanding of group emotionality, which should improve our ability to predict group emotionality and to change these emotions when they are undesired.

Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network

We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.

Model of strategy control for delayed panic spread in emergencies

In emergencies similar to virus spreading in an epidemic model, panic can spread in groups, which brings serious bad effects to society. To explore the transmission mechanism and decision-making behavior of panic, a government strategy was proposed in this paper to control the spread of panic. First, based on the SEIR epidemiological model, considering the delay effect between susceptible and exposed individuals and taking the infection rate of panic as a time-varying variable, a SEIR delayed panic spread model was established and the basic regeneration number of the proposed model was calculated. Second, the control strategy was expressed as a state delayed feedback and solved using the exact linearization method of nonlinear control system; the control law for the system was determined, and its stability was proven. The aim was to eradicate panic from the group so that the recovered group tracks the whole group asymptotically. Finally, we simulated the proposed strategy of controlling the spread of panic to illustrate our theoretical results.

Scaffolding cooperation in human groups with deep reinforcement learning

Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and simulation methods to train a 'social planner' capable of making recommendations to create or break connections between group members. The strategy that it develops succeeds at encouraging pro-sociality in networks of human participants (N = 208 participants in 13 groups) playing for real monetary stakes. Under the social planner, groups finished the game with an average cooperation rate of 77.7%, compared with 42.8% in static networks (N = 176 in 11 groups). In contrast to prior strategies that separate defectors from cooperators (tested here with N = 384 in 24 groups), the social planner learns to take a conciliatory approach to defectors, encouraging them to act pro-socially by moving them to small highly cooperative neighbourhoods.

The spatial effect of digital economy on public psychological resilience during the diffusive crisis

Purpose

To explore whether the digital economy has spatial effects and spatial heterogeneity on public psychological resilience during the diffusive crisis and to analyze the specific impact mechanisms.

Methods

This study is based on the Baidu Search Index from 2011 to 2020 and the provincial panel data of 30 provinces in China. It constructs measures of public psychological resilience and digital economy development level and employs the spatial Durbin model to empirically analyze the relationship between the two, revealing their spatial impact.

Results

(1) Public psychological resilience exhibits a spatial distribution characterized by high values in the west, medium values in the central region, and low values in the east, while the digital economy development level shows a "U"-shaped spatial structure with high levels in the eastern and western regions and low levels in the middle; (2) The digital economy development level in a local region has a negative effect on the public psychological resilience of that region, while the digital economy development level in surrounding regions has a positive spatial spillover effect on the local region's public psychological resilience.

Conclusion

It is essential to strengthen crisis management, focus on the coordinated development of the digital economy in different regions, share the benefits of digital society development more equitably and broadly, and further improve the psychological resilience of regions under the context of digital economy development.

Does Misery Love Company? Multilevel Relationships between Perceived Age Discrimination and Happiness among Older Europeans

The present study examines how contextual age discrimination moderates the individual-level association between perceived age discrimination and happiness among older Europeans. In this endeavor, we test two opposing views: 1) the "social norm" hypothesis that predicts the association between perceived age discrimination and happiness to become weaker in areas with a higher average level of age discrimination; and 2) conversely the "contagion effect" hypothesis that predicts the association to grow stronger in such areas. Using data from the European Social Survey (2008), we estimate two- and three-level mixed effects models to test these opposing hypotheses. Our findings from multilevel analysis lend support to the social norm hypothesis. Specifically, the negative link between perceived age discrimination and happiness is weaker in subnational regions where the proportion of victims of age discrimination is higher.

Spreading positive change: Societal benefits of meditation

Research over the past decades has revealed a variety of beneficial effects of meditation training. These beneficial effects span the levels of health and well-being, cognition, emotion, and social behavior. Around the same time, sociologists have shown that traits and outcomes on the individual level have the potential to spread in communities over three or more degrees. This means, for example, that changes can spread from one person to the next, and on to yet another person. Here, we propose that meditation-induced changes may likewise spread through the social networks of meditation practitioners. Such spreading may happen by positively influencing others through prosocial actions, improved cognitive functioning, and increased positive affect. Positive affective states and their underlying physiological correlates may also be shared in the literal sense. We argue that the spreading of positive meditation effects could provide the basis for collective responses to some of the urgent challenges we face in our current time and society and call for future meditation research to examine the phenomenon.

Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis

Background

Occupational burnout is a type of psychological syndrome. It can lead to serious mental and physical disorders if not treated in time. However, individuals tend to conceal their genuine feelings of occupational burnout because such disclosures may elicit bias from superiors. This study aims to explore a novel method for estimating occupational burnout by elucidating its links with social, lifestyle, and health status factors.

Methods

In this study 5,794 participants were included. Associations between occupational burnout and a set of features from a survey was analyzed using Chi-squared test and Wilcoxon rank sum test. Variables that are significantly related to occupational burnout were grouped into four categories: demographic, work-related, health status, and lifestyle. Then, from a network science perspective, we inferred the colleague's social network of all participants based on these variables. In this inferred social network, an exponential random graph model (ERGM) was used to analyze how occupational burnout may affect the edge in the network.

Results

For demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 0.01) was a significant factor as well. For health and chronic diseases variables, self-rated health status, hospitalization history in the last 3 years, arthritis, cardiovascular diseases, high blood lipid, breast diseases, and other chronic diseases were all associated with occupational burnout significantly (p < 0.01). Breakfast frequency, dairy consumption, salt-limiting tool usage, oil-limiting tool usage, vegetable consumption, pedometer (step counter) usage, consuming various types of food (in the previous year), fresh fruit and vegetable consumption (in the previous year), physical exercise participation (in the previous year), limit salt consumption, limit oil consumption, and maintain weight were also significant factors (p < 0.01). Based on the inferred social network among all airport workers, ERGM showed that if two employees were both in the same occupational burnout status, they were more likely to share an edge (p < 0.0001).

Limitation

The major limitation of this work is that the social network for occupational burnout ERGM analysis was inferred based on associated factors, such as demographics, work-related conditions, health and chronic diseases, and behaviors. Though these factors have been proven to be associated with occupational burnout, the results inferred by this social network cannot be warranted for accuracy.

Conclusion

This work demonstrated the feasibility of identifying people at risk of occupational burnout through an inferred colleague's social network. Encouraging staff with lower occupational burnout status to communicate with others may reduce the risk of burnout for other staff in the network.

The Loneliness of Low-Income Older Adults in a Federal Volunteering Program: A Network Perspective

Volunteering has been associated with increased social interactions and reduced feelings of loneliness among older adults. However, a growing number of social network analyses (SNA) conducted in the general population outside of volunteering contexts suggest that lonely individuals tended to interact with other lonely individuals in the network, reinforcing loneliness through peer associations. To better understand the psychosocial impact of peer interactions among older adults within volunteer programs, this study examines how older adults' loneliness is correlated with their peers' loneliness within the Senior Companions Program (SCP). This study collected information on the social networks within an SCP in a Midwest Metropolitan and feelings of loneliness among low-income Russian, Khmer, Somali, Nepali, and English-speaking older volunteers (N = 41). A linear network autocorrelation model (LNAM) was constructed to quantify how volunteers' loneliness is correlated with their peers' loneliness within SCP. The LNAM results indicated that less lonely volunteers tended to make friends with lonelier volunteers (ρ = -.06, p < .05) in SCP even when accounting for statistical controls. The finding that more and less lonely individuals connect indicates an altruistic tendency for less lonely individuals to interact with those who are lonelier. This may be an important pathway by which volunteering addresses loneliness.

Social capital I: measurement and associations with economic mobility

Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health^1–8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers⁹, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date^10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality^12–14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.

Analyses of data on 21 billion friendships from Facebook in the United States reveal associations between social capital and economic mobility.

The Transfer Model and Guidance Strategy of Netizens' Emotions

In the context of the COVID-19 pandemic, a large amount of information is gathered on Internet platforms, through which people express their opinions and vent their emotions. Emotional guidance of netizens has become an important part of social governance during turbulences caused by a so-called "infodemic". This study focuses on the evolution and interaction of netizens' emotions after the occurrence of network public opinion events. First, the transfer model of netizens' emotions is constructed, and the significance of each parameter in the model is studied through simulation. Then, based on the model, we put forward the optimization method and quantitative method of guidance strategy of netizens' emotions. Finally, the empirical study proves the effectiveness of the model, which can provide a theoretical basis for the emotional guidance strategies after the outbreak of network public opinion events.

The social transmission of stress in animal collectives

The stress systems are powerful mediators between the organism's systemic dynamic equilibrium and changes in its environment beyond the level of anticipated fluctuations. Over- or under-activation of the stress systems' responses can impact an animal's health, survival and reproductive success. While physiological stress responses and their influence on behaviour and performance are well understood at the individual level, it remains largely unknown whether-and how-stressed individuals can affect the stress systems of other group members, and consequently their collective behaviour. Stressed individuals could directly signal the presence of a stressor (e.g. via an alarm call or pheromones), or an acute or chronic activation of the stress systems could be perceived by others (as an indirect cue) and spread via social contagion. Such social transmission of stress responses could then amplify the effects of stressors by impacting social interactions, social dynamics and the collective performance of groups. As the neuroendocrine pathways of the stress response are highly conserved among vertebrates, transmission of physiological stress states could be more widespread among non-human animals than previously thought. We therefore suggest that identifying the extent to which stress transmission modulates animal collectives represents an important research avenue.

Analysis of continuous-time Markovian ɛ-SIS epidemics on networks

We analyze continuous-time Markovian ɛ-SIS epidemics with self-infections on the complete graph. The majority of the graphs are analytically intractable, but many physical features of the ɛ-SIS process observed in the complete graph can occur in any other graph. In this work, we illustrate that the timescales of the ɛ-SIS process are related to the eigenvalues of the tridiagonal matrix of the SIS Markov chain. We provide a detailed analysis of all eigenvalues and illustrate that the eigenvalues show staircases, which are caused by the nearly degenerate (but strictly distinct) pairs of eigenvalues. We also illustrate that the ratio between the second-largest and third-largest eigenvalue is a good indicator of metastability in the ɛ-SIS process. Additionally, we show that the epidemic threshold of the Markovian ɛ-SIS process can be accurately approximated by the effective infection rate for which the third-largest eigenvalue of the transition matrix is the smallest. Finally, we derive the exact mean-field solution for the ɛ-SIS process on the complete graph, and we show that the mean-field approximation does not correctly represent the metastable behavior of Markovian ɛ-SIS epidemics.

Is Teachers' Depression Contagious to Students? A Study Based on Classes' Hierarchical Models

Background

According to the theory of emotional contagion, emotions in one person can trigger similar emotions in groups within social networks. In schools, the class just like a small social network, that teachers' emotion, such as depression, might be contagious to their students. However, until now there is few studies reporting this issue. This study aims to explore whether teachers' depression be contagious to students and what mechanics behind the phenomenon.

Methods

Using Children's depression and cognitive scales to assess 2,579 students, meanwhile using teachers' depression and emotional labor scales assess 529 teachers. The nested data from 112 classes were analyzed.

Results

Teachers' depression was positively correlated with emotional labor surface and deep acting, and teachers' depression cross-level predicted students' depression inversely. For teachers with higher levels of depression, the teacher's deep acting affected their students' depression significantly, the more effortful the teachers' deep acting, the lower the degree of the students' depression, however, for teachers with lower levels of depression, the deep acting was not significant.

Conclusion

The results maybe state that depression in teachers is not readily transmitted to students, one of reasons is that teachers' emotional labor may alleviate the influence of their depression on students. However, considered that teachers' emotional labor was positively correlated with their depression, the teachers' emotional labor may be like a double-edged sword, while alleviating the influence of teachers' depression on students, it also deteriorated their own depression, making it impossible sustainable. For students' depression interventions based in school, including teachers maybe a better selection.

Modelling Maternal Depression: An Agent-Based Model to Examine the Complex Relationship between Relative Income and Depression

Depression is a major public health concern among expectant mothers in Canada. Income inequality has been linked to depression, so interventions for reducing income inequality may reduce the prevalence of maternal depression. The current study aims to simulate the effects of government transfers and increases to minimum wage on depression in mothers. We used agent-based modelling techniques to identify the predicted effects of income inequality reducing programs on maternal depression. Model parameters were identified using the All Our Families cohort dataset and the existing literature. The mean age of our sample was 30 years. The sample was also predominantly white (78.6%) and had at least some post-secondary education (89.1%). When income was increased by just simulating an increase in minimum wage, the proportion of depressed mothers decreased by 2.9% (p < 0.005). Likewise, simulating the Canada Child Benefit resulted in a 5.0% decrease in the prevalence of depression (p < 0.001) and Ontario’s Universal Basic Income pilot project resulted in a simulated 5.6% decrease in the prevalence of depression (p < 0.001). We also assessed simulated changes to the mother’s social networks. Progressive income policies and increasing social networks are predicted to decrease the probability of depression.

A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives

Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.

Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19

Bursts and collective emotion have been widely studied in social physics field where researchers use mathematical models to understand human social dynamics. However, few researches recognize and separately analyze the internal and external influence on burst behaviors. To bridge this gap, we introduce a non-parametric approach to classify an interevent time series into five scenarios: random arrival, endogenous burst, endogenous non-burst, exogenous burst and exogenous non-burst. In order to process large-scale social media data, we first segment the interevent time series into sections by detecting change points. Then we use the rule-based algorithm to classify the time series based on its distribution. To validate our model, we analyze 27.2 million COVID-19 related comments collected from Chinese social media between January to October 2020. We adopt the emotion category called Profile of Mood States which consists of six emotions: Anger, Depression, Fatigue, Vigor, Tension and Confusion. This enables us to compare the burst features of different collective emotions during the COVID-19 period. The burst detection and classification approach introduced in this paper can also be applied to analyzing other complex systems, including but not limited to social media, financial market and signal processing.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users' different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users' interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users' informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

Contagious Depression: Automatic Mimicry and the Mirror Neuron System - A Review

Contagious depression is a theory proposing that depression can be induced or triggered by our social environment. This theory is based on emotional contagion, the idea that affective states can be transferred during social interaction, since humans can use emotional contagion to communicate feelings and emotions in conscious and unconscious ways. This review presents behavioral, physiological, and neuroanatomical aspects of two essential contagious depression mechanisms, automatic mimicry and the mirror neuron system.

Panic Spreading Model with Different Emotions under Emergency

Emotion plays an important role in decision making. In an emergency, panic can spread among crowds through person-to-person communications and can cause harmful effects on society. The aim of this paper is to propose a new theoretical model in the context of epidemiology to describe the spread of panic under an emergency. First, according to divisions in personality in the context of psychology, groups are divided into a level-headed group and an impatient group. Second, individuals in the two groups have unique personalities. Thus, the level-headed group only infects within the group, while the impatient group considers emotional infection within the group and cross infection between the groups. Then, a nonlinear infection rate is used to describe the probability of infection after an infected person contacts a susceptible person, which is more in line with the real situation. After that, the level-headed group–impatient group nonlinear SIRS panic spreading model is developed. Stable analysis of the model is obtained using the Lyapunov function method to study the stability of the panic-free equilibrium and panic-permanence equilibrium. Finally, simulations are carried out to dynamically describe the spread process of group emotional contagion.

Emotional Contagion in the Online Depression Community

Negative emotions are prevalent in the online depression community (ODC), which potentially puts members at risk, according to the theory of emotional contagion. However, emotional contagion in the ODC has not been confirmed. The generalized estimating equation (GEE) was used to verify the extent of emotional contagion using data from 1548 sample users in China's popular ODC. During interaction, the emotional themes were analyzed according to language use. The diurnal patterns of the interaction behaviors were also analyzed. We identified the susceptible groups and analyzed their characteristics. The results confirmed the occurrence of emotional contagion in ODC, that is, the extent to which the user's emotion was affected by the received emotion. Our study also found that when positive emotional contagion occurred, the replies contained more hopefulness, and when negative emotional contagion occurred, the replies contained more hopelessness and fear. Second, positive emotions were easier to spread, and people with higher activity in ODC were more susceptible. In addition, nighttime was an active period for user interaction. The results can help community managers and support groups take measures to promote the spread of positive emotions and reduce the spread of negative emotions.

Multi-thread hierarchical deep model for context-aware sentiment analysis

Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.

The Social Brain and Emotional Contagion: COVID-19 Effects

BACKGROUND AND OBJECTIVES

Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease, responsible for a global pandemic that began in January 2020. Human/COVID-19 interactions cause different outcomes ranging from minor health consequences to death. Since social interaction is the default mode by which individuals communicate with their surroundings, different modes of contagion can play a role in determining the long-term consequences for mental health and emotional well-being. We examined some basic aspects of human social interaction, emphasizing some particular features of the emotional contagion. Moreover, we analyzed the main report that described brain damage related to the COVID-19 infection. Indeed, the goal of this review is to suggest a possible explanation for the relationships among emotionally impaired people, brain damage, and COVID-19 infection.

RESULTS

COVID-19 can cause several significant neurological disorders and the pandemic has been linked to a rise in people reporting mental health problems, such as depression and anxiety. Neurocognitive symptoms associated with COVID-19 include delirium, both acute and chronic attention and memory impairment related to hippocampal and cortical damage, as well as learning deficits in both adults and children.

CONCLUSIONS

Although our knowledge on the biology and long-term clinical outcomes of the COVID-19 infection is largely limited, approaching the pandemic based on lessons learnt from previous outbreaks of infectious diseases and the biology of other coronaviruses will provide a suitable pathway for developing public mental health strategies, which could be positively translated into therapeutic approaches, attempting to improve stress coping responses, thus contributing to alleviate the burden driven by the pandemic.

Fear of Virus or of Competitors? The Decision Rationales of Financial Managers Under COVID-19

This paper surveyed 422 financial managers before the number of novel coronavirus (COVID-19) infections in China peaked and used path analysis to study the risk decision-making mechanisms of financial managers. The study found that whether financial managers developed coping strategies depends on their assessment of potential business revenue losses. There are two transmission paths: the direct effect refers to the risk perception directly caused by COVID-19, while the indirect effect refers to managers’ fear that they will not make timely adjustments or will make judgment errors, resulting in the loss of competitive advantage. It is worth noting that the indirect effect exceeds the direct effect, which indicates that financial managers are more rational than ordinary people in dealing with COVID-19, that they are relatively more concerned about competitor changes, and that they may even view COVID-19 as an important opportunity to obtain a better competitive position.

Corona and value change. The role of social media and emotional contagion

People share their emotions on social media and evidence suggests that in times of crisis people are especially motivated to post emotional content. The current Coronavirus pandemic is such a crisis. The online sharing of emotional content during the Coronavirus crisis may contribute to societal value change. Emotion sharing via social media could lead to emotional contagion which in turn could facilitate an emotional climate in a society. In turn, the emotional climate of a society can influence society's value structure. The emotions that spread in the current Coronavirus crisis are predominantly negative, which could result in a negative emotional climate. Based on the dynamic relations of values to each other and the way that emotions relate to values, a negative emotional climate can contribute to societal value change towards values related to security preservation and threat avoidance. As a consequence, a negative emotional climate and the shift in values could lead to a change in political attitudes that has implications for rights, freedom, privacy and moral progress. Considering the impact of social media in terms of emotional contagion and a longer-lasting value change is an important perspective in thinking about the ethical long-term impact of social media technology.

Addition of an Emotionally Stable Node in the SOSa-SPSa Model for Group Emotional Contagion of Panic in Public Health Emergency: Implications for Epidemic Emergency Responses

Sentiment contagion is similar to an infectious disease that spreads in a crowd. In this study, we explore the law of emotional infection under sudden public events by SIR model. The paper adds an emotionally stable node and establishes a group emotional infection model of U-SOSPa-SPSOa model. Simulation results show that our model is reasonable and can better explain the entire contagion process by considering four groups (unsusceptible-susceptible-optimistic-pessimistic) of people. Our theoretical results show: When the pessimists were below the critical value of 0.34, the number of negative emotional groups first increased and then decreased. As the proportion increases, the emotional peak of pessimists increases. The cure probability θ_o has the least influence on the P(t), and at the same time, under the action of θ_p, the P(t) reaches the stable state first. The increase of the risk coefficient can promote the pessimist infection. When the degree of risk is low, the rate of emotional infection is increased. When the degree of risk is high, the rate of infection is slowed. Therefore, system customizers and related managers can improve the efficiency of stable groups, adjust the proportion of initial negative emotions, control the infection of the spontaneous infection process, and directly deal with negative emotions. They can carry out treatment and other means to stabilize group emotions and maintain social stability.

Time dependence of susceptible-infected-susceptible epidemics on networks with nodal self-infections

The average fraction of infected nodes, in short the prevalence, of the Markovian ɛ-SIS (susceptible-infected-susceptible) process with small self-infection rate ɛ>0 exhibits, as a function of time, a typical "two-plateau" behavior, which was first discovered in the complete graph K_{N}. Although the complete graph is often dismissed as an unacceptably simplistic approximation, its analytic tractability allows to unravel deeper details, that are surprisingly also observed in other graphs as demonstrated by simulations. The time-dependent mean-field approximation for K_{N} performs only reasonably well for relatively large self-infection rates, but completely fails to mimic the typical Markovian ɛ-SIS process with small self-infection rates. While self-infections, particularly when their rate is small, are usually ignored, the interplay of nodal self-infection and spread over links may explain why absorbing processes are hardly observed in reality, even over long time intervals.

Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement

From fake news to innovative technologies, many contagions spread as complex contagions via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source.1 Contrarily, biological agents such as Ebola or measles are typically thought to spread as simple contagions.2 Here, we demonstrate that these different spreading mechanisms can have indistinguishable population-level dynamics once multiple contagions interact. In the social context, our results highlight the challenge of identifying and quantifying spreading mechanisms, such as social reinforcement,3 in a world where an innumerable amount of ideas, memes and behaviors interact. In the biological context, this parallel allows the use of complex contagions to effectively quantify the non-trivial interactions of infectious diseases.

Vulnerable robots positively shape human conversational dynamics in a human-robot team

Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot-human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game (n = 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

Explosive phase transition in susceptible-infected-susceptible epidemics with arbitrary small but nonzero self-infection rate

The ɛ-susceptible-infected-susceptible (SIS) epidemic model on a graph adds an independent, Poisson self-infection process with rate ɛ to the "classical" Markovian SIS process. The steady state in the classical SIS process (with ɛ=0) on any finite graph is the absorbing or overall-healthy state, in which the virus is eradicated from the network. We report that there always exists a phase transition around τ_{c}^{ɛ}=O(ɛ^{-1/N-1}) in the ɛ-SIS process on the complete graph K_{N} with N nodes, above which the effective infection rate τ>τ_{c}^{ɛ} causes the average steady-state fraction of infected nodes to approach that of the mean-field approximation, no matter how small, but not zero, the self-infection rate ɛ is. For τ<τ_{c}^{ɛ} and small ɛ, the network is almost overall healthy. The observation was found by mathematical analysis on the complete graph K_{N}, but we claim that the phase transition of explosive type may also occur in any other finite graph. We thus conclude that the overall-healthy state of the classical Markovian SIS model is unstable in the ɛ-SIS process and, hence, unlikely to exist in reality, where "background" infection ɛ>0 is imminent.

Digital Emotion Contagion

People spend considerable time on digital media, and are thus often exposed to expressions of emotion by other people. This exposure can lead their own emotion expressions becoming more similar to those of others, a process we refer to as 'digital emotion contagion'. This article reviews the growing literature on digital emotion contagion. After defining emotion contagion, we suggest that one unique feature of digital emotion contagion is that it is mediated by digital media platforms that are motivated to upregulate user emotions. We then turn to measurement, and consider the challenges of demonstrating that digital emotion contagion has occurred, and how these challenges have been addressed. Finally, we call for a greater focus on understanding when emotion contagion effects are likely to be strong versus weak or nonexistent.

Social Transmission of Leadership Preference: Knowledge of Group Membership and Partisan Media Reporting Moderates Perceptions of Leadership Ability From Facial Cues to Competence and Dominance

While first impressions of dominance and competence can influence leadership preference, social transmission of leadership preference has received little attention. The capacity to transmit, store and compute information has increased greatly over recent history, and the new media environment may encourage partisanship (i.e., "echo chambers"), misinformation and rumor spreading to support political and social causes and be conducive both to emotive writing and emotional contagion, which may shape voting behavior. In our pre-registered experiment, we examined whether implicit associations between facial cues to dominance and competence (intelligence) and leadership ability are strengthened by partisan media and knowledge that leaders support or oppose us on a socio-political issue of personal importance. Social information, in general, reduced well-established implicit associations between facial cues and leadership ability. However, as predicted, social knowledge of group membership reduced preferences for facial cues to high dominance and intelligence in out-group leaders. In the opposite-direction to our original prediction, this "in-group bias" was greater under less partisan versus partisan media, with partisan writing eliciting greater state anxiety across the sample. Partisanship also altered the salience of women's facial appearance (i.e., cues to high dominance and intelligence) in out-group versus in-group leaders. Independent of the media environment, men and women displayed an in-group bias toward facial cues of dominance in same-sex leaders. Our findings reveal effects of minimal social information (facial appearance, group membership, media reporting) on leadership judgments, which may have implications for patterns of voting or socio-political behavior at the local or national level.

Are the "Right" People Selected for First Disclosures About Suicidal Thoughts? Exploring What We Know About Advance Care Planning in the Context of Safety Planning

Nominating primary confidants (i.e., safety plan nominees) is central to safety planning for suicide but there is a dearth of research about such confidants-a serious oversight given their high-leverage position to affect pathways into clinical care. Informed by advance care planning (ACP) work, this study examined differences between social network members who were and were not identified as primary confidants on attributes that could impact their effectiveness in a helping role. Using social network methodology, information was collected from 41 individuals with affective disorders about the 332 people and relationships in their networks. Logistic regression analysis revealed that being perceived as capable of managing stress and prioritizing the participant's best interests over one's own increased the odds of being a primary confidant. Clinicians working with suicidal individuals may benefit from integrating ACP principles-which recognize the thoughtful selection of nominees based on specific attributes-into their safety planning procedures.

Understanding Fear of Zika: Personal, Interpersonal, and Media Influences

Fear of infectious disease often motivates people to protect themselves. But, it can also produce negative bio-social-psychological effects whose severity is on par with those of the disease. The WHO declaration of Zika as a world health crisis presented an opportunity to study factors that bring about fear. Beginning nine days after the WHO announcement, data were gathered from women aged 18-35 living in the southern United States (N = 719). Respondents reported experiencing fear of Zika at levels akin to those reported following other significant crises/disasters (e.g., the terrorist attacks of 9/11). Fear increased as a function of (1) personal, but not other-relevance, (2) frequency of media exposure, but not media content, and (3) frequency of interpersonal exposure and interpersonal content. It is argued that media and interpersonal message sources may be innately predisposed to amplify, rather than attenuate, risk.

Cluster-based network proximities for arbitrary nodal subsets

The concept of a cluster or community in a network context has been of considerable interest in a variety of settings in recent years. In this paper, employing random walks and geodesic distance, we introduce a unified measure of cluster-based proximity between nodes, relative to a given subset of interest. The inherent simplicity and informativeness of the approach could make it of value to researchers in a variety of scientific fields. Applicability is demonstrated via application to clustering for a number of existent data sets (including multipartite networks). We view community detection (i.e. when the full set of network nodes is considered) as simply the limiting instance of clustering (for arbitrary subsets). This perspective should add to the dialogue on what constitutes a cluster or community within a network. In regards to health-relevant attributes in social networks, identification of clusters of individuals with similar attributes can support targeting of collective interventions. The method performs well in comparisons with other approaches, based on comparative measures such as NMI and ARI.

Exposure, hazard, and survival analysis of diffusion on social networks

Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission.

Detecting concurrent mood in daily contact networks: an online participatory cohort study with a diary approach

OBJECTIVES

This paper examines how people express personal mood concurrently with those connected with them by one or two degrees of separation.

DESIGN

Participatory cohort study.

SETTING

Online contact diary.

PARTICIPANTS

133 participants kept online diaries for 7 months in 2014, which included 127 455 contacts with 12 070 persons.

MAIN OUTCOME MEASURES

Diary keepers rated a contacted person's mood during each specific contact, as well as the strength of ties between any pairs of such contacted persons. Such rich information about ties and contacts enable us to construct a complete contact network for each diary keeper, along with the network members' mood and tie strength. We calculate one's overall mood by that person's average mood score during the study period and take the shortest path between any given pair of contacted persons as the degree of separation. We further assume that two connecting persons in a contact network have made contact with each other during the study period, which allows us to examine whether and how personal moods occur concurrently within these contact networks.

RESULTS

Using mixed-effects models while controlling for covariates at individual, tie and contact levels, we show that personal mood score positively and significantly correlates with the average mood among those directly tied to the person. The same effect remains positive and significant for those connected to the person by two degrees, although the effect size is reduced by about one-half. The mood of anyone separated by more than two degrees is statistically irrelevant.

CONCLUSIONS

Applying network perspectives and rich data at both tie and contact levels to inquiries about subjective well-being, the current study sheds new light on how an improved diary approach can help explain the sophisticated ways in which individuals express their personal moods concurrently during social interactions in everyday life, contact by contact.

A New Method for Inducing a Depression-Like Behavior in Rats

Contagious depression is a phenomenon that is yet to be fully recognized and this stems from insufficient material on the subject. At the moment, there is no existing format for studying the mechanism of action, prevention, containment, and treatment of contagious depression. The purpose of this study, therefore, was to establish the first animal model of contagious depression. Healthy rats can contract depressive behaviors if exposed to depressed rats. Depression is induced in rats by subjecting them to several manipulations of chronic unpredictable stress (CUS) over 5 weeks, as described in the protocol. A successful sucrose preference test confirmed the development of depression in the rats. The CUS-exposed rats were then caged with naïve rats from the contagion group (1 naïve rat/2 depressed rats in a cage) for an additional 5 weeks. 30 social groups were created from the combination of CUS-exposed rats and naïve rats. This proposed depression-contagion protocol in animals consists mainly of cohabiting CUS-exposed and healthy rats for 5 weeks. To ensure that this method works, a series of tests are carried out - first, the sucrose preference test upon inducing depression to rats, then, the sucrose preference test, alongside the open field and forced-swim tests at the end of the cohabitation period. Throughout the experiment, rats are given tags and are always returned to their cages after each test. A few limitations to this method are the weak differences recorded between the experimental and control groups in the sucrose preference test and the irreversible traumatic outcome of the forced swim test. These may be worth considering for suitability before any future application of the protocol. Nonetheless, following the experiment, naïve rats developed contagion depression after 5 weeks of sharing the same cage with the CUS-exposed rats.

The vicarious effects of discrimination: How partner experiences of discrimination affect individual health

Little is known about how discriminatory experiences are associated with interpersonal relationships-specifically whether one person's experience of discrimination has psychological effects on their partner and their relationship (i.e., vicarious effects). Using dyadic data analyses, we examined actor and partner effects of discriminatory experiences on self-rated health, chronic illness, depression, and relationship strain in a sample of 1,949 couples (3,898 participants). Actor and partner discrimination were associated with poorer health, greater depression, and greater relationship strain. These effects were mediated by higher levels of relationship strain. Our findings provide insight into the effects of direct and vicarious experiences of discrimination on interpersonal relationships.

Qualitative Exploration of the Potential for Adverse Events When Using an Online Peer Support Network for Mental Health: Cross-Sectional Survey

BACKGROUND

Online peer support networks are a growing area of mental health support for offering social connection, identity, and support. However, it has been reported that not all individuals have a positive experience on such networks. The potential for adverse events within a moderated online peer support network is a new area of research exploration.

OBJECTIVE

The objective of the study was to determine if use of an online moderated peer networks leads to adverse events for users.

METHODS

Four biannual online surveys (October 2014 to March 2016) were conducted by a large national UK mental health charity, with users of their online peer support network exploring personal safety, moderation, experiences on the site, and how the site could be improved. Data were analyzed using thematic analysis by 2 independent researchers using a priori themes: negative experiences of moderation, social exclusion, contagion, negative interactions with other users, online relationships, co-rumination and collusion, and other.

RESULTS

In total, 2353 survey responses were logged with 197 (8.37%) documenting an adverse event of negative experience. A dominant theme of negative experiences of moderation emerged (73/197, 37.1%) with evidence of social exclusion (50/197, 25.4%). Reading user posts was shown to be a cause of worry and distress for a few users, and analysis highlighted several instances of depressogenic and emotional contagion as well as some limited evidence of behavioral contagion (46/197, 23.4%). Very limited evidence of co-rumination (1/197, 0.5%) and no evidence of collusion were identified.

CONCLUSIONS

Evidence of adverse events was identified at low levels in the sample of respondents, although we have no comparison data to indicate if levels are low compared with comparable platforms. Not all users of online peer support networks find them wholly beneficial. Research must explore what works for whom. The next stage of service development should consider which users may be likely to receive no benefit, or even deteriorate, as a result of using the service.

Spreading of components of mood in adolescent social networks

Recent research has provided evidence that mood can spread over social networks via social contagion, but that, in seeming contradiction to this, depression does not. Here, we investigate whether there is evidence for the individual components of mood (such as appetite, tiredness and sleep) spreading through US adolescent friendship networks while adjusting for confounding by modelling the transition probabilities of changing mood state over time. We find that having more friends with worse mood is associated with a higher probability of an adolescent worsening in mood and a lower probability of improving, and vice versa for friends with better mood, for the overwhelming majority of mood components. We also show, however, that this effect is not strong enough in the negative direction to lead to a significant increase in depression incidence, helping to resolve the seeming contradictory nature of existing research. Our conclusions, therefore, link in to current policy discussions on the importance of subthreshold levels of depressive symptoms and could help inform interventions against depression in high schools.

Computational Psychometrics for Modeling System Dynamics during Stressful Disasters

Disasters can be very stressful events. However, computational models of stress require data that might be very difficult to collect during disasters. Moreover, personal experiences are not repeatable, so it is not possible to collect bottom-up information when building a coherent model. To overcome these problems, we propose the use of computational models and virtual reality integration to recreate disaster situations, while examining possible dynamics in order to understand human behavior and relative consequences. By providing realistic parameters associated with disaster situations, computational scientists can work more closely with emergency responders to improve the quality of interventions in the future.

Dyadic interdependence of psychosocial outcomes among haematological cancer survivors and their support persons

PURPOSE

This study aimed to explore the dyadic relationships between unmet need, depression, and anxiety in people diagnosed with haematological cancer and their support persons.

METHODS

Adult survivors (18 years+) who had been diagnosed with a haematological cancer were recruited to a cross-sectional mailed survey via five state cancer registries in Australia. Participating survivors invited a support person to also complete a survey. Structural equation modelling was used to explore the relationships among survivor and support person self-reported depression, anxiety, and unmet needs.

RESULTS

Of the 4299 eligible haematological cancer survivors contacted by the registries, 1511 (35%) returned a completed survey as did 1004 support persons. There were 787 dyads with complete data. After adjusting for age, gender, rurality, cancer type, and whether the support person was a relative, positive correlations were found between survivor and support person scores for depression (p = 0.0029) and unmet needs (p < 0.001), but not anxiety scores (p = 0.075). Survivor unmet needs were significantly related to support person depression (p = 0.0036). Support person unmet needs were significantly related to a higher depression score for survivors (p = 0.0067). Greater support person unmet needs were significantly related to a higher anxiety score for survivors (p = 0.0083). Survivor unmet needs did not have a significant relationship to support person anxiety (p = 0.78).

CONCLUSION

Unmet needs may mediate the interdependence of psychosocial experiences for survivors and support persons, although a longitudinal study is required to confirm causality. Addressing unmet needs may be a potential target for improving outcomes for both groups.