The structure of online social networks mirrors those in the offline world

We use data on frequencies of bi-directional posts to define edges (or relationships) in two Facebook datasets and a Twitter dataset and use these to create ego-centric social networks. We explore the internal structure of these networks to determine whether they have the same kind of layered structure as has been found in offline face-to-face networks (which have a distinctively scaled structure with successively inclusive layers at 5, 15, 50 and 150 alters). The two Facebook datasets are best described by a four-layer structure and the Twitter dataset by a five-layer structure. The absolute sizes of these layers and the mean frequencies of contact with alters within each layer match very closely the observed values from offline networks. In addition, all three datasets reveal the existence of an innermost network layer at ∼1.5 alters. Our analyses thus confirm the existence of the layered structure of ego-centric social networks with a very much larger sample (in total, >185,000 egos) than those previously used to describe them, as well as identifying the existence of an additional network layer whose existence was only hypothesised in offline social networks. In addition, our analyses indicate that online communities have very similar structural characteristics to offline face-to-face networks.     

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.     

Comparing Networks Across Space and Time, Size and Species

We describe and illustrate methodology for comparing networks from diverse settings. Our empirical base consists of 42 networks from four kinds of species (humans, nonhuman primates, nonprimate mammals, and birds) and covering distinct types of relations such as influence, grooming, and agonistic encounters. The general problem is to determine whether networks are similarly structured despite their surface differences. The methodology we propose is generally applicable to the characterization and comparison of network–level social structures across multiple settings, such as different organizations, communities, or social groups, and to the examination of sources of variability in network structure. We first fit a p^* model (Wasserman and Pattison 1996) to each network to obtain estimates for effects of six structural properties on the probability of the graph. We then calculate predicted tie probabilities for each network, using both its own parameter estimates and the estimates from every other network in the collection. Comparison is based on the similarity between sets of predicted tie probabilities. We then use correspondence analysis to represent the similarities among all 42 networks and interpret the resulting configuration using information about the species and relations involved. Results show that similarities among the networks are due more to the kind of relation than to the kind of animal.     

Measuring social dynamics in a massive multiplayer online game

Quantification of human group-behavior has so far defied an empirical, falsifiable approach. This is due to tremendous difficulties in data acquisition of social systems. Massive multiplayer online games (MMOG) provide a fascinating new way of observing hundreds of thousands of simultaneously socially interacting individuals engaged in virtual economic activities. We have compiled a data set consisting of practically all actions of all players over a period of 3 years from a MMOG played by 300,000 people. This large-scale data set of a socio-economic unit contains all social and economic data from a single and coherent source. Players have to generate a virtual income through economic activities to ‘survive’ and are typically engaged in a multitude of social activities offered within the game. Our analysis of high-frequency log files focuses on three types of social networks, and tests a series of social-dynamics hypotheses. In particular we study the structure and dynamics of friend-, enemy- and communication networks. We find striking differences in topological structure between positive (friend) and negative (enemy) tie networks. All networks confirm the recently observed phenomenon of network densification. We propose two approximate social laws in communication networks, the first expressing betweenness centrality as the inverse square of the overlap, the second relating communication strength to the cube of the overlap. These empirical laws provide strong quantitative evidence for the Weak ties hypothesis of Granovetter. Further, the analysis of triad significance profiles validates well-established assertions from social balance theory. We find overrepresentation (underrepresentation) of complete (incomplete) triads in networks of positive ties, and vice versa for networks of negative ties. Empirical transition probabilities between triad classes provide evidence for triadic closure with extraordinarily high precision. For the first time we provide empirical results for large-scale networks of negative social ties. Whenever possible we compare our findings with data from non-virtual human groups and provide further evidence that online game communities serve as a valid model for a wide class of human societies. With this setup we demonstrate the feasibility for establishing a ‘socio-economic laboratory’ which allows to operate at levels of precision approaching those of the natural sciences.All data used in this study is fully anonymized; the authors have the written consent to publish from the legal department of the Medical University of Vienna.     

Analyzing event stream dynamics in two-mode networks: An exploratory analysis of private communication in a question and answer community

Information about social networks can often be collected as event stream data. However, most methods in social network analysis are defined for static network snapshots or for panel data. We propose an actor oriented Markov process framework to analyze the structural dynamics in event streams. Estimated parameters are similar to what is known from exponential random graph models or stochastic actor oriented models as implemented in SIENA. We apply the methodology on a question and answer web community and show how the relevance of different kinds of one- and two-mode network structures can be tested using a new software.     

The Role of Social Networks in Getting a Job

This article reviews recent research on the effect s of social networks on access to job information and getting a job in the United States. Drawing on network ties from friends, family members, acquaintances, employers, or coworkers can improve the job search because individuals gain access to and make use of their network’s social capital. While this job searching strategy can result in a successful job search for some, not all job seekers benefit from reliance on social networks. We spotlight research that documents how reliance on social networks as a means to find work can actually maintain sex and racial/ethnic inequality at work. We discuss research documenting the important role social networks play in the job acquisition process. The last half of this review focuses on several new developments in the literature that promise to further our understanding of social networks’ lasting effects on employment outcomes.     

Beyond interviewer effects in the standardized measurement of ego-centric networks

The effects which interviewers exert on the collection of ego-centric networks have recently come into the focus of methodological considerations. Studies consistently show that the size of networks varies depending on the interviewer. We would like to expand on this research strand by pointing to different aspects which have so far gone unremarked in the discussion. First, size is mainly analysed as a network measure which is influenced during data collection, while other common measures such as network density or composition have not received sufficient consideration. Second, large-scale surveys using face-to-face interviews usually allocate interviewers to a single sampling point. Differences between sampling points (locality effects) are attributed to interviewer effects. Hence, we disentangle the effects of the locality and interviewer. Third, the discussion on interviewer effects often follows an “actor-oriented” consideration of how data collection situations are structured by interviewers. Expanding this approach from a relational perspective, we consider the relationship between the interviewers and respondents and whether this relationship influences the collection of network data. To test our hypotheses about the influence of interviewers, the locality and the interviewer-respondent relationship on different network measures, we use data from the 2010 German General Social Survey (n = 2827 respondents, n = 220 interviewers). The multilevel analyses show that the relationship between the interviewer and the respondent is not very relevant. Furthermore, the analyses show that interviewers have an influence on the network size but not on measures of their composition. However, evidence on the prevalence of locality or interviewer effects is mixed. Finally, homophilous interviewer-respondent relationships have very little effect on network characteristics. We find evidence of training and fatigue effects on network size. However, much of the variation in network size caused by the interviewer still remains unexplained. We draw conclusions on how to organize interview situations in surveys.     

The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic,multidimensional social network data

The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N₁ = 226, N₂ = 261, N₃ = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition.     

Re-evaluating standards of human subjects protection for sensitive health data in social media networks

This study addresses ethical questions about conducting health science research using network data from social media platforms. We provide examples of ethically problematic areas related to participant consent, expectation of privacy, and social media networks. Further, to illustrate how researchers can maintain ethical integrity while leveraging social media networks, we describe a study that demonstrates the ability to use social media to identify individuals affected by cancer. We discuss best practices and ethical guidelines for studying social media network data, including data collection, analysis, and reporting.     

A comparison of email networks and off-line social networks: A study of a medium-sized bank

Recently there has been a surge in the availability of online data concerning the connections between people, and these online data are now widely used to map the social structure of communities. There has been little research, however, on how these new types of relational data correspond to classical measures of social networks. To fill this gap, we contrast the structure of an email network with the underlying friendship, communication, and advice seeking networks. Our study is an explorative case study of a bank, and our data contains emails among employees and a survey of the ego networks of the employees. Through calculating correlations with QAP standard errors and estimating exponential random graph (ERG) models, we find that although the email network is related to the survey-based social networks, email networks are also significantly different: while off-line social networks are strongly shaped by gender, tenure, and hierarchical boundaries, the role of these boundaries are much weaker in the email network.     

Dynamic social media affiliations among UK politicians

Inter-personal affiliations and coalitions are an important part of politicians’ behaviour, but are often difficult to observe. Since an increasing amount of political communication now occurs online, data from online interactions may offer a new toolkit to study ties between politicians; however, the methods by which robust insights can be derived from online data require further development, especially around the dynamics of political social networks. We develop a novel method for tracking the evolution of community structures, referred to as ‘multiplex community affiliation clustering’ (MCAC), and use it to study the online social networks of Members of Parliament (MPs) and Members of the European Parliament (MEPs) in the United Kingdom. Social interaction networks are derived from social media (Twitter) communication over an eventful 17-month period spanning the UK General Election in 2015 and the UK Referendum on membership of the European Union in 2016. We find that the social network structure linking MPs and MEPs evolves over time, with distinct communities forming and re-forming, driven by party affiliations and political events. Without including any information about time in our model, we nevertheless find that the evolving social network structure shows multiple persistent and recurring states of affiliation between politicians, which align with content states derived from topic analysis of tweet text. These findings show that the dominant state of partisan segregation can be challenged by major political events, ideology, and intra-party tension that transcend party affiliations.     

Group based trajectories of network formation and dynamics

In this paper, we propose the application of a semi-parametric statistical methodology called Group-Based Developmental Trajectory Analysis to studying the dynamics of social networks. We begin with a discussion of theoretical problems in network analysis that may benefit from this approach. Next, we describe the methodology and how it can be applied to dyadic network data as well as aggregated node level data. We then demonstrate the methodology by analyzing the Newcomb Fraternity and the van de Bunt student data sets. Finally, we conclude with a discussion of potential directions for further research.     

Tracking phantastic objects: A computer algorithmic investigation of narrative evolution in unstructured data sources

We develop social network and “relative sentiment shift” analysis techniques to study how financial narratives influence financial markets. First, we analyze Reuters News articles focusing on narratives about Fannie Mae. Second, we analyze Broadband and Energy narratives in the Enron Corporation email database. Combining datasets we show that phantastic object narratives can be detected and tracked as they develop and spread through networks to lead to a disconnect between narrative and underlying “reality”. The methods may be applicable to other text datasets to create early warnings.     

Bridging semantic and social network analyses: the case of the hashtag #precisamosfalarsobreaborto (we need to talk about abortion) on Twitter

This paper presents an innovative method design that combines semantic with social network analysis in order to measure opinion leadership in social networking sites in a more accurate way. We used this method to assess the efficacy of the TPM magazine in disseminating its pro-decriminalization of abortion frames (contained in the cover story of its 148th issue) that were associated with the hashtag #precisamosfalarsobreaborto (a trending topic in November 2014). The data were collected from Twitter through the data-mining application NodeXL (N?=?1010). A content analysis of a random sample was carried out (N?=?376; margin of error?=?4%; confidence interval?=?95%; Krippendorff’s alpha?=?0.661). Using the software Gephi, we plotted the data on a socio-semantic graph, which indicates that (a) the border of the social network does not represent a semantic gap with the center and (b) despite the network being extremely like-minded, one of its hubs appears to be what we conceptualize as a hotspot of contestation. We discuss how future research may replicate and refine our methodology to handle population datasets and big data as well.     

To tell the truth: Measuring concordance in multiply reported network data

Social network data must accurately reflect actors’ relationships to properly estimate network features. Here, we examine multiple reports of sexual, drug-sharing and social tie data on high-risk networks in Colorado Springs. By comparing multiple reports on the same ties, we can evaluate the reliability of this study's network data. Our findings suggest that these data have a high level of reporting agreement. From these findings, we discuss implications for analysis of these and similar data and provide suggestions for future social network data collection efforts.     

When is brokerage negatively associated with economic benefits? Ethnic diversity,competition, and common-pool resources

There is a growing body of literature positively linking dimensions of social capital to economic benefits. Yet recent research also points to a potential “dark side” of social capital, where over-embeddedness in networks and the pressures associated with brokerage are hypothesized to constrain actors, having a negative effect on economic outcomes. This dichotomy suggests that context is important, yet the overwhelming majority of existing empirical evidence stems from socially homogenous populations in corporate and organizational settings, limiting a broader understanding of when and how context matters. We advance this discourse to a socially fragmented, ethnically diverse common-pool resource system where information is highly valuable and competition is fierce. Merging several unique datasets from Hawaii's pelagic tuna fishery, we find that network prominence, i.e., being well connected locally, has a significant, positive effect on economic productivity. In contrast, we find that brokerage, defined here as ties that bridge either structurally distinct or ethnically distinct groups, has a significant, negative effect. Taken together, our results provide empirical support to widespread claims of the value of information access in common-pool resource systems, yet suggest that in ethnically diverse, competitive environments, brokers may be penalized for sharing information across social divides. Our results thus contribute to an emerging theory on the fragile nature of brokerage that recognizes its potential perils and the importance of context.     

Social network structure and the trade-off between social utility and economic performance

We put forward a computational multi-agent model capturing the impact of social network structure on individuals’ social trust, willingness to cooperate, social utility and economic performance. Social network structure is modeled as four distinct social capital dimensions: degree, centrality, bridging and bonding social capital. Model setup draws from socio-economic theory and empirical findings based on our novel survey dataset. Results include aggregate-level comparative statics and individual-level correlations. We find, inter alia, that societies that either are better connected, exhibit a lower frequency of local cliques, or have a smaller share of family-based cliques, record relatively better aggregate economic performance. As long as family ties are sufficiently valuable, there is a trade-off between aggregate social utility and economic performance, and small world networks are then socially optimal. We also find that in dense networks and trustful societies, there is a trade-off between individual social utility and economic performance; otherwise both outcomes are positively correlated in the cross section.     

Change in Covert Social Movement Networks: The ‘Inner Circle’ of the Provisional Irish Republican Army

In this paper, we contribute to both the growing body of literature on social movement networks and the growing body of literature on change in networks by exploring patterns and mechanisms of change within the network of the ‘inner circle’ of the Provisional Irish Republican Army. Specifically, we focus upon the period between 1969 (when it formed) and 1988 (the last point for which we have been able to gather good data). Our primary aims are substantive. We want to know how this network changed over time. In addition, however, our analysis identifies changes which other analysts might look for in their networks and offers methodological suggestions for those who, like us, find that their networks do not meet the assumptions of mainstream approaches to modelling network dynamics. There is a further dimension to the paper, however. We are studying a covert social movement network. This is a special type of movement network whose organisation and dynamics are predicted to vary from other movement networks. Some have suggested that they are inclined to be relatively static because the need for trust within them is so great and the risk to whatever they hold secret so considerable when new ties are formed that their members tend only to recruit within the pool of their pre-existing ties and actively seek to minimise recruitment and the formation of new ties. One of the aims of our paper was to determine whether and to what extent this is so.