The missing link: Social network analysis in migration and transnationalism

The focus on social networks in migration studies marked a significant departure of understanding. Social networks are not only a mechanism through which the migration process is patterned, but they also have broader implications for migrants and non-migrants alike. Despite the fact that the network character of migration processes has long been recognized in migration studies, for a long time, Social Network Analysis has not been applied. Taking this scholarly omission as a starting point, we seek in this special issue to discuss recent research into social networks and migration that use SNA approaches.     

Social network engineering and race in a police academy: A longitudinal analysis

This research examined an attempt to facilitate racial integration by populating squads (i.e., workgroups) in a police academy with mixes of recruits that reflected the racial demographics of the larger cohort. This was part of the social infrastructure of the academy. Additionally, a fixed seating arrangement was considered as a second element of academy infrastructure capable of impacting racial integration. We examined the consequences of these academy components over time with regard to race by combining ethnographic accounts with social network data collected throughout the academy and using a variety of network analytic tools. These consequences with regard to race were examined as a part of social network evolution. The academy's social arrangements did accelerate the creation of social knowledge of recruits about each other and the formation of friendship ties both within and between races. However, our results point to clear limitations to such infrastructural engineering and have implications for both recruitment to police academies and dealing with race. They shed light also on processes of homophily and group composition over time and have implications for studying social networks.     

Social network analysis,structuration theory and the holism-individualism debate

Attempts at developing the theoretical side of social network analysis have not generated a satisfactory account of its methodological suppositions and implications. This paper clarifies the network conception of the relationship between human action and social structure by documenting the convergence of social netwoek analysis with structuration theory on the principle of methodological individualism. It then uses the convergence argument to suggest a new role for social network analysis in the methodological restructuring of modern social theory.     

Social network analysis for assessing college-aged adults' health: A systematic review

Objective: Social network analysis (SNA) is a useful, emerging method for studying health. College students are especially prone to social influence when it comes to health. This review aimed to identify network variables related to college student health and determine how SNA was used in the literature. Participants: A systematic review of relevant literature was conducted in October 2015. Methods: Studies employing egocentric or whole network analysis to study college student health were included. We used Garrard's Matrix Method to extract data from reviewed articles (n = 15). Results: Drinking, smoking, aggression, homesickness, and stress were predicted by network variables in the reviewed literature. Methodological inconsistencies concerning boundary specification, data collection, nomination limits, and statistical analyses were revealed across studies. Conclusions: Results show the consistent relationship between network variables and college health outcomes, justifying further use of SNA to research college health. Suggestions and considerations for future use of SNA are provided.     

Polarized public opinion responding to corporate social advocacy: Social network analysis of boycotters and advocators

Despite growing attention to corporate social-political advocacy, little is known about how publics mobilize and establish relationships in social media when firms are involved in hot-button issues. Using the social network approach, this study examines a network structure which emerged around boycotting and advocating for Starbucks and Budweiser when these two brands responded to President Donald Trump’s immigration ban executive order in 2017. The study identified three unique characteristics in the boycotters’ networks. The boycotters appeared not only in the aggregated brand boycotting networks, but also in the advocators’ networks. In addition, boycotters in Budweiser and Starbucks networks were engaged in boycotting other brands or organizations which were opposed to Republicans or President Trump’s policy. Finally, the network of boycotters was very dense and highly connected among subgroups while that of advocators was sparse. Theoretical and practical implications are discussed.     

Social context and network formation: An experimental study

Recently, there has been increasing interest in determining which social network structures emerge as a consequence of the conscious actions of actors. Motivated by the belief that “networks matter” in reaching personal objectives, it is a natural assumption that actors try to optimize their network position. Starting from the notion that an optimal network position depends on the social context, we examine how actors change their networks to reach better positions in various contexts. Distinguishing between three social contexts (a neutral context, a context in which closed triads are costly, and a context in which closed triads are beneficial), theoretical results predict that emerging networks are contingent on the incentives that are present in these contexts. Experiments are used to test whether networks that are theoretically predicted to be stable are also stable experimentally. We find that emerging networks correspond to a large extent with the predicted networks. Consequently, they are contingent on the incentives present in various social contexts. In addition, we find that subjects tend to form specific stable networks with a higher probability than predicted, namely, efficient networks and networks in which everyone is equally well off.     

Social network analysis: New ethical approaches through collective reflexivity. Introduction to the special issue of Social Networks

Research in social network analysis (SNA) faces unprecedented ethical challenges today due to both technological developments (‘big’ data) and a growing tendency toward institutionalization of ethics governance. We argue that a suitable response requires a more comprehensive approach to SNA ethics, and we identify two main paths toward this goal. First, we need to recognize the breadth of the subject, no longer limited to anonymity and consent. Second, we need to encourage the active participation and contribution of SNA scholars. We advocate a need for greater reflexivity at both individual and collective levels, whereby researchers can learn from their own and their peers’ experiences, and foster a more ethically ‘virtuous’ approach within the whole SNA community.     

Social network analysis with respondent-driven sampling data: A study of racial integration on campus

This paper presents respondent-driven sampling (RDS) as a viable method of sampling and analyzing social networks with survey data. RDS is a network based sampling and analysis method that provides a middle ground compliment to ego-centric and saturated methods of social network analysis. The method provides survey data, similar to ego-centric approaches, on individuals who are connected by behaviorally documented ties, allowing for macro-level analysis of network structure, similar to that supported by saturated approaches. Using racial interaction of university undergraduates as an empirical example, the paper examines whether and to what extent racial diversity at the institutional level is reflected as racial integration at the interpersonal level by testing hypotheses regarding the quantity and quality of cross-race friendships. The primary goal of this article, however, is to introduce RDS to the network community and to stimulate further research toward the goal of expanding the analytical capacity of RDS. Advantages, limitations, and areas for future research to network analysis using RDS are discussed.     

Social construction of public relations knowledge by academic institutions worldwide: A bibliometric network analysis

This VOSviewer-enabled bibliometric study examines scientific knowledge construction in public relations research since 1955. A co-citation analysis indicates that while sustaining its cross-disciplinary nature, public relations increasingly relies on scholarship generated within its own discipline, with Public Relations Review, Journal of Public Relations Research and Journalism & Mass Communication Quarterly most cited academic sources. A citation analysis of author affiliations revealed a number of findings, including universities with the highest count of publications, university pairs that tend to cite each other, as well as regional university groups and large clusters within a broader, citation-based network. Among the 108 most productive academic institutions from four continents included into the study, U.S.-based universities produced the highest number of publications in nine peer-reviewed journals. Cross-referencing citation relationships among university clusters in different world regions illustrates an ongoing pattern of globalized construction of public relations knowledge.     

Social network and family business: Uncovering hybrid family firms

What does it mean for a private enterprise in China to be embedded in a family? Our purpose here is twofold: (1) use social network analysis to describe what it means for a firm to be embedded in a family, (2) reveal from the application a new kind of firm, not family, yet akin to family. Armed with data on a large probability sample of private enterprises — a third of which meet ownership and employment criteria of being family businesses — we uncover a category of “hybrid family firms” that look modern in the style of firms that exclude family, but operate socially in ways similar to family firms. Our conclusion from summary statistics on the sample is that there are no differences in average performance level or network advantage for the three categories of businesses: family firms, hybrid family firms, and family-excluded firms. The fact that CEOs of family firms and hybrid family firms more often turn to family as key business contacts is a fact about network composition that raises no question about network mechanisms. Whether the CEO turns to more or fewer family contacts, government help is more likely with stronger political connections, and business success and survival are more likely with a large, open network. That said, the look-modern, act-traditional hybrid family firms stand alone in prospering with a CEO embedded in a closed business network. Recognition of hybrid family firms adds to the literature’s illustrations of social network analysis used to distinguish types of businesses and business people, and extends the population of organizations within which governance and strategy are likely to be better understood when viewed through a family logic.     

Social network resources and management of hypertension

Hypertension is one of the most prevalent chronic diseases among older adults, but rates of blood pressure control are low. In this article, we explore the role of social network ties and network-based resources (e.g., information and support) in hypertension diagnosis and management. We use data from the National Social Life, Health, and Aging Project to identify older adults with undiagnosed or uncontrolled hypertension. We find that network characteristics and emotional support are associated with hypertension diagnosis and control. Importantly, the risks of undiagnosed and uncontrolled hypertension are lower among those with larger social networks-if they discuss health issues with their network members. When these lines of communication are closed, network size is associated with greater risk for undiagnosed and uncontrolled hypertension. Health care utilization partially mediates associations with diagnosis, but the benefits of network resources for hypertension control do not seem to stem from health-related behaviors.     

Social network boundaries and tricky to access populations: a qualitative approach

Social network researchers are routinely faced with the boundary specification problem, namely, where to place limits on nodes that constitute a social network. The complexity of the issue varies; some networks are easily delineated, while others are not. For instance, if we were interested in exploring the relations among older people in an elder care home, then ‘residence’ would define actor membership. However, where a network exists with ambiguous, perhaps unstable, outer limits and its population proves tricky to access, significant challenges arise for the researcher. In this article, I consider ‘the boundary specification problem’ as it relates to an amorphous, ‘tricky-to-access’ population of cross-community youth leaders in North Belfast, Northern Ireland. This article will review current strategies and approaches for bounding social networks, assess the applicability of each and provide practical insights and extended ideas around delineating ambiguous, unstable, elusive structures composed of ‘tricky to access’ populations.     

Predictors and outcomes of social network compositions: A compositional structural equation modeling approach

Proportions of a total, including social network compositions (proportions of partner, family, friends, etc.) lie in a restricted space, which challenges statistical analysis. Network compositions can be both dependent and explanatory variables and are usually measured with error by survey instruments. Structural equation models make it possible to correct measurement error bias. Coenders et al. (2011) fitted a factor analysis model to transformed network compositions. In this article, we use another transformation called an isometric log-ratio and we extend the model to include predictors and outcomes. The findings and hypotheses in the literature can be reformulated with isometric log-ratios in a more interpretable manner. For instance, we find relationships of gender with partner support, of education and extraversion with friend support, and of family support with tie multiplexity and closeness.     

Who benefits from network analysis: ethics of social network research

The success of social network research (SNR) has led to expectations that in addition to academic research, SNR can introduce people to one another, solve organizational problems, map the epidemiology of AIDS, and catch criminals and terrorists. Since SNR requires that names of both respondents and their contacts be collected and used in most analyses, Institutional Review Boards become very concerned. Experiences of the author, participants in the 2003 Sun Belt Conference and the Social Network List Serve illustrate ethical issues. Proper handling of the data and the analysis, including complete control by the investigator can virtually eliminate harm to respondents and those they nominate, though perhaps not to the satisfaction of IRBs. On the benefit side, academic researchers always benefit, organizations, society and science may benefit, but individual respondents rarely do.     

Social media approaches to modeling wildfire smoke dispersion: spatiotemporal and social scientific investigations

Wildfires have significant effects on human populations, economically, environmentally, and in terms of their general well-being. Smoke pollution, in particular, from either prescribed burns or uncontrolled wildfires, can have significant health impacts. Some estimates suggest that smoke dispersion from fire events may affect the health of one in three residents in the United States, leading to an increased incidence of respiratory illnesses such as asthma and pulmonary disease. Scarcity in the measurements of particulate matter responsible for these public health issues makes addressing the problem of smoke dispersion challenging, especially when fires occur in remote regions. Crowdsourced data have become an essential component in addressing other societal problems (e.g., disaster relief, traffic congestion) but its utility in monitoring air quality impacts of wildfire events is unexplored. In this study, we assessed if user-generated social media content can be used as a complementary source of data in measuring particulate pollution from wildfire smoke. We found that the frequency of daily tweets within a 40,000?km² area was a significant predictor of PM2.5 levels, beyond daily and geographic variation. These results suggest that social media can be a valuable tool for the measurement of air quality impacts of wildfire events, particularly in the absence of data from physical monitoring stations. Also, an analysis of the semantic content in people’s tweets provided insight into the socio-psychological dimensions of fire and smoke and their impact on people residing in, working in, or otherwise engaging with affected areas.     

Social capital exchanges in voluntary associations and work organizations: A network perspective

Organizations have been an important setting in which social capital exchanges (SCEs) occur, but little work has been done to distinguish two predominant species of organizations in the social network literature: voluntary associations and formal work organizations. Addressing this lacuna, this article comparatively examines how the two organizational species differ in (1) how two prominent types of SCEs operate (restricted and generalized exchange), as well as (2) the analytical approaches and methodological tools for studying SCEs (boundary-specification, sampling, network designs, tie-recording methods) and their adherent implications for network structure (networking conditions and homophily). This article concludes by identifying methodological and theoretical challenges for studying SCEs in organizations (conceptualizing organizations as units, underappreciating meaning-making and methodological triangulation, and examining contagion in organizational networks in an age of digitalization) and developing recommendations for overcoming them.     

Social network and content analysis of the North American Carbon Program as a scientific community of practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of communities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.