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Online gaming has emerged as a popular activity providing a social outlet for millions. However, implications of online game networks for mental health remain disputed. Concepts of bridging social capital and bonding social capital may help characterize protective factors within social networks. This study aims to examine the associations between social capital derived from online versus in-person networks and mental health indicators among gamers.

Online gamers (n = 301) completed an online survey assessing their social networks (both in-person and through online gaming) and mental health indicators (depressive symptoms, anxiety, social isolation, perceived social support). Social network analysis was used to analyze bridging (network size, effective size, heterogeneity, weak ties) and bonding (closeness, frequent contact, confiding, connection quality) social capital. Separate linear regression models evaluated associations between bridging and bonding social capital for both online and in-person networks and depressive symptoms, anxiety, social support and social isolation.

In-person network characteristics showed the strongest associations with mental health outcomes. Greater average closeness and frequent confiding in the in-person network predicted lower isolation and fewer depressive symptoms. More diverse relationship types also correlated with lower depression. For online networks, closeness and confiding ties associated only with less isolation and greater support, not depressive symptoms, or anxiety.

While online gaming networks provide some degree of social support, in-person social capital exhibited stronger associations with mental health. This reinforces the importance of face-to-face relationships for emotional well-being. Findings suggest helping gamers cultivate close bonds offline. However, online connections still matter and should not be discounted.

Social relationships play an important role in organizational entrepreneurship. They are crucial to entrepreneurs’ decisions because, despite the bleeding-edge technological advancements observed nowadays, entrepreneurs as human beings will always strive to be social. During the COVID-19 pandemic many companies moved activities into the virtual world and as a result offline Social relationships became rarer, but as it turns out, even more valuable, likewise, the inter-organizational cooperation enabling many companies to survive.

This chapter aims to develop knowledge about entrepreneurs’ SR and their links with inter-organizational cooperation. The results of an integrative systematic literature review show that the concept of Social relationships, although often investigated, lacks a clear definition, conceptualization, and operationalization. This chapter revealed a great diversity of definitions for Social relationships, including different scopes of meaning and levels of analysis. The authors identify 10 building blocks and nine sources of entrepreneurs’ Social relationships. The authors offer an original typology of Social relationships using 12 criteria. Interestingly, with regard to building blocks, besides those frequently considered such as trust, reciprocity and commitment, the authors also point to others more rarely and narrowly discussed, such as gratitude, satisfaction and affection. Similarly, the authors discuss the varied scope of sources, including workplace, family/friendship, past relationships, and ethnic or religious bonds. The findings of this study point to a variety of links between Social relationships and inter-organizational cooperation, including their positive and negative influences on one another. These links appear to be extremely dynamic, bi-directional and highly complex.

The study seeks to understand what teachers know about students’ friend networks and how they use that information for instruction.

This qualitative study relied on interviews and sociograms that teachers drew of students’ friend networks.

Our data suggest that teachers’ awareness of their students’ friend networks varies by their experience and their exposure to students. Also, their use of this information for instruction coalesces around dimensions of grouping and social support.

This study took place at one school. To more deeply understand what teachers perceive about their students’ friend networks and how they use that information for instruction, more studies could be done with teachers in more schools.

Implications might suggest developing teachers’ social competence to support their students’ learning and development.

While studies cited in this paper have explored teachers’ knowledge of students’ social networks, this study builds on this work by exploring how that information can be useful for instruction. In addition, this study explores the use of teachers drawing sociograms as representations of what they know about students’ friend networks.

By drawing on the research paradigm of collective action that occurs in physical space, the present study aims to explore the antecedent predictors of network social mobilization – feeling of injustice – and discuss the emotional mechanism of this prediction: mediating effect of anger and resentment.

Micro-blog postings about network social mobilization were collected to develop the dictionary of codes of fairness, anger and resentment. Then, according to the dictionary, postings on Sina Weibo were coded and analyzed.

The feeling of injustice predicted network social mobilization directly. The predictive value was 27% and 33%, respectively during two analyses. The feeling of injustice also predicted social mobilization indirectly via anger and resentment. In other words, anger and resentment account for the active mechanism in which the feeling of injustice predicts network social mobilization. Mediating effect value was 29.63% and 33.33% respectively.

This study is our first exploration to use python language to collect data from human natural language pointing on micro-blog, a large number of comments of netizen about certain topic were crawled, but a small portion of the comments could be coded into analyzable data, which results in a doubt of the reliability of the study. Therefore, we should put the established model under further testing.

In the cyberspace, this study confirms the mechanism of network social mobilization, expands and enriches the research on social mobilization and deepens the understanding of social mobilization.

This study provides an empirical evidence to understand the network social mobilization, and it gives us the clue to control the process of network social mobilization.

This study uses the Python language to write Web crawlers to obtain microblog data and analyze the microblog content for word segmentation and matching thesaurus. It has certain innovation.

In this study, the authors examine whether social capital embedded in individuals' social networks is connected to employees' long-term income development in Finland.

Analyses are based on 25–35-year-old employees from the Finnish Living Conditions Survey of 1994 combined with register data on earned incomes from 1995 to 2016. The authors used questions addressing the frequency of meeting parents or siblings, spending free time with co-workers and participation in associational, civic or other societal activities as measures of the extent of network capital. Ordered logistic model was used to examine whether the size and composition of social networks differ by gender and socio-economic status. Linear growth curve models were employed to estimate the effect of social capital on long-term income development.

Results indicate minor differences in network composition according to gender, but large differences between socio-economic groups. The authors found that income development was faster for those who participated in civic activities occasionally or who met their relatives or co-workers on a monthly basis, that is, for the “middle group”.

Results are generalizable only to Finnish or Nordic welfare state context. The authors’ measures of social capital come from cross-sectional survey. Thus, the authors are not able to address the stability or accumulation of social capital during life course. This restriction will probably cause the authors’ analysis to underestimate the true effect of social capital on earned incomes.

Moderate-level investments to network capital seem to be the most beneficial with regard to the long-term income development.

The study results give support to the idea that social capital can be transformed into economic capital. The results also imply that in economic terms it is important to balance diverse forms of social capital. At the policy level, a special emphasis should be directed to employees with low-socio-economic position. These people are especially vulnerable as their low level of income is combined with network composition that hinders their further income development.

The combined survey and register data give unique insight on how the social capital embedded in individuals' social networks is connected with long-term income development.

The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics.

A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model.

The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods.

This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

Significant resources of time, money and expertise are invested in leadership development programs, and networking is often cited as a benefit of participation in these programs. Previous research has traditionally focused on leadership as an individual attribute, but researchers and practitioners are increasingly recognizing leadership as a social process. Social capital has emerged as an important theme in leadership research, and networking and relationship-building are important steps in enhancing social capital. Based on this review of recent literature, I conclude that the relationship between social capital and leadership is well documented, but we have an incomplete understanding of the dynamic nature of this relationship, and lack sufficient evidence to support a causal assertion that one leads to the other. Researchers and practitioners should develop new leadership development program evaluation methods and designs, in the context of social capital, to answer these questions.

Participants in leadership development programs take part in multiple developmental experiences that can influence the composition of their social network and enhance social capital. However, further investment in such practices may be limited because little is known about the relationship between leadership development approaches, networking ability, and social capital. This study explores how common developmental approaches may contribute to social capital, taking into consideration the role networking ability plays in this relationship. This descriptive, correlational study included a sample of graduates (N= 231) from 15 statewide agricultural-based leadership development programs. Our findings reveal that: 1) Networking is an antecedent to social capital, 2) skill building and personal growth approaches to leadership development are significant predictors of networking ability, and 3) networking ability is a significant predictor of social capital.

Social Network Analysis has been posited as a useful technique to determine if leadership development programs are an effective intervention in developing social ties and enhancing connectivity among leaders in an organization. Evaluations can examine the extent to which the leadership development programs create and catalyze peer networks. This study used Social Network Analysis to evaluate the development of a peer leadership network and resulting relationships among leaders participating in a leadership development program. Several predictions were made about the development of participants’ task, career, and social networks, generally predicting enhanced “esprit de corps” with their peer leaders over time. Thirty top executives in local public health were selected to participate in a 12-month national leadership development training program. Peer network development was documented at three time points across the programmatic year at 6-month intervals. The results demonstrated that while leaders’ social networks increased over time, friendship networks increased more slowly than did acquaintance networks. The task-related networks involving interactions to solve problems, and career networks for seeking advice and support increased over time, with task-related and advice-related networks stabilizing by the end of the second workshop. Implications for developing peer leadership networks are discussed.

The authors would like to acknowledge the Robert Wood Johnson Foundation and the National Association for County and City Health Officials and for their support of this research.

This chapter examines the structure and sentiment of the Twitter response to Nathan Broad's naming as the originator of an image-based sexual abuse incident following the 2017 Australian Football League Grand Final. Employing Social Network Analysis to visualize the hierarchy of Twitter users responding to the incident and Applied Thematic Analysis to trace the diffusion of differing streams of sentiment within this hierarchy, we produced a representation of participatory social media engagement in the context of image-based sexual abuse. Following two streams of findings, a model of social media user engagement was established that hierarchized the interplay between institutional and personal Twitter users. In this model, it was observed that the Broad incident generated sympathetic and compassionate discourses among an articulated network of social media users. This sentiment gradually diffused to institutional Twitter users – or Reference accounts – through the process of intermedia agenda-setting, whereby the narrative of terrestrial media accounts was altered by personal Twitter users over time.