Search

Purpose: The purpose of this paper is to investigate students' social networking strategies based on learning values in the social context of Iranian universities. Design/methodology/approach: This research has used the case study method to investigate students' networked learning (NL) strategies among students of four different universities in the context of Iran. They were explored by semistructured in-depth interviews about the nature or current conditions of their study life. The data were analyzed by the qualitative analysis method based on the systematic approach of Strauss and Corbin (1998). Findings: The researchers in this study found the special concept of NL in the social context: "NL as a continuous process of thinking to understand the environment through multiple/ diverse careers of learning knowledge and skills for environmental modeling towards creating added-value." According to this definition, various networked strategies were identified as follows: 1) interactive and multidimensional searching, 2) multidimensional studying and learning, 3) environmental and social management, 4) studying and social wandering. Research limitations/implications: Designing dynamic curriculum, which integrates various careers of learning, multidimensional knowledge and abilities and subject matters with environmental events and social structures of the society. Given the exploratory nature of the research, the paper cannot provide empirically justified findings. Originality/value: The research had identified diverse approaches of networking strategies, which do not appear just as a positive instrument to improve learning conditions. However, there is such an extreme degree of social wandering among students in universities that makes their efforts useless.

Purpose: For a university to be a prime mover for sustainability transformation, all units of the university should contribute. However, organizational change in educational institutions is often studied by examining specific domains such as research or operation in isolation. This results in a less-than-complete picture of the potential for university-wide change. In contrast, this paper aims to examine the network of social relations that determine the diffusion and sustainability of change efforts across a university. The authors use McGill University (Canada) as a model system to study the network of actors concerned with sustainability to learn how this network influences the penetration of sustainability throughout the university. Design/methodology/approach: To explore the existing social structure, the authors use an innovative approach to illuminate the influence of social structure on organizational change efforts. Using a mixed methods approach combining social network analysis with qualitative interview data, the authors examine the influence of the social structure on sustainability transformation at McGill University. The authors conducted 52 interviews between January and April 2019 with representatives of different sustainability groups at the university across six domains (research, education, administration, operations, connectivity and students). Findings: The authors find that McGill University has a centralized system with a low density. The network is centralized around the Office of Sustainability. The limited cross-domain interaction appears to be a result of differences in motivation and priorities. This leads to a network that has many actors but only a limited number of connections between them. The quality of the relationships is often utilitarian, with only a few relationships aiming for support and mutual growth. Originality/value: This study brings together social network analysis, sustainability transformation and higher education in a new way. It also illustrates the complexity of guiding a large organization, such as a university, toward a sustainability transformation. Furthermore, it reveals the importance of considering each part of the university as part of an interconnected network rather than as isolated components.

Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.

Calls for improvement of undergraduate science education have resulted in numerous initiatives that seek to improve student learning outcomes by promoting changes in faculty teaching practices. Although many of these initiatives focus on individual faculty, researchers consider the academic department to be a highly productive focus for creating change. In this paper, we argue that it is important for change agents to understand the informal social structure of the academic department and introduce social network analysis techniques to uncover this social structure. Examples are given from data collected in five academic departments. A short sociometric web survey was used to ask instructors to identify colleagues with whom they discuss teaching and the frequency of their discussions. Techniques of social network analysis are used to determine the current state of the department, target participants for a change initiative, and anticipate the spread of new teaching ideas. Results suggest that these techniques identify informal structures that would otherwise be hidden and that may be important for planning change initiatives.

Whereas technical standards and Standard Setting Organizations (SSOs) are omnipresent and essential to mass production and communications, relatively little is formally known about the propensity of firms' decisions to belong to certain SSOs. An understanding of such propensities can explain why some firms join SSOs (and others do not) and have implications for the regulation of SSOs. This paper uses a social network analysis technique to categorize/place firms in SSO communities and then empirically analyzes their propensities to belong to SSOs. We concentrate our study on standard setting organizations' features and their intellectual property rights (IPR) policies such as licensing rules, disclosure requirements, as well as the features of the decision process of standards. Using data on more than 1060 member firms as participants in 28 SSOs, we are able to uniquely graph the membership of firms in SSOs by highlighting some important characteristics through community detection. The results provide some novel insights into why firms might choose certain SSO communities over others. [ABSTRACT FROM AUTHOR]

This paper relies upon the 'what's the problem represented to be?' approach to policy analysis to interrogate key representations of human trafficking implicit in the UK government's anti-trafficking policy. It identifies six policy vectors, or representations, of human trafficking embedded within the policy, including organized crime, 'illegal' immigration, and victim assistance as three primary vectors; sexual exploitation/prostitution, poverty in countries of victims' origin, and isolated instances of labour law infringements as three secondary vectors. In addition, a series of assumptions, which underlie the current interpretation of trafficking, are also identified. By exploring what the problem of human trafficking is represented to be, the paper also provides an insight into what remains obscured within the context of the dominant policy frameworks. In doing so, it highlights the role of state-capital entanglements in normalizing exploitation of trafficked, smuggled and 'offshored' labour, and critiques the UK's anti-trafficking policy for manufacturing doubt as to the structural causes of human trafficking within the context of neoliberalism. [ABSTRACT FROM AUTHOR]

Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a method called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE method improves the overall prediction performance over the baseline models—that use demographics and physical attributes—by 38%, 65%, 55%, and 54% for the wellness states—stress, happiness, positive attitude, and self-assessed health—considered in this paper. [ABSTRACT FROM AUTHOR]

Micro-blogging systems have become one of the most important ways for information sharing. Network structure and users’ interactions such as forwarding behaviors have aroused considerable research attention, while mention, as a key feature in micro-blogging platforms which can improve the visibility of a message and direct it to a particular user beyond the underlying social structure, is seldom studied in previous works. In this paper, we empirically study the mention effect in information diffusion, using the dataset from a population-scale social media website. We find that users with high number of followers would receive much more mentions than others. We further investigate the effect of mention in information diffusion by examining the response probability with respect to the number of mentions in a message and observe a saturation at around 5 mentions. Furthermore, we find that the response probability is the highest when a reciprocal followship exists between users, and one is more likely to receive a target user’s response if they have similar social status. To illustrate these findings, we propose the response prediction task and formulate it as a binary classification problem. Extensive evaluation demonstrates the effectiveness of discovered factors. Our results have consequences for the understanding of human dynamics on the social network, and potential implications for viral marketing and public opinion monitoring. [ABSTRACT FROM AUTHOR]

Dynamic Stochastic General Equilibrium (DSGE) models are widely used as a tool for policy decision-making. These models lost their fame when they could not predict the crisis in 2008 and could not address policy problems afterward. Meanwhile, the Agent-Based Modelling (ABM) approach emerged as an alternative to DSGE models. Between 2000 and 2020, this study examined scholarly research on the topic of ABM in economics. The information is gathered using the SCOPUS database. Numerous bibliometric indicators are provided, including the total number of publications and citations. The study reveals that agent-based modelling in economics research has grown in recent years. The majority of active research occurs in countries such as the United States of America, and collaboration allows researchers to reach out to many more countries. ABM has the potential to be applied in a wide range of economic fields. ABM also necessitates research into its own development to be used to better understand economic phenomena. [ABSTRACT FROM AUTHOR]

This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account. [ABSTRACT FROM AUTHOR]

Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section. [ABSTRACT FROM AUTHOR]

Individuals often make decisions in a social environment where social influence can impact on people's decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual's belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process. [ABSTRACT FROM AUTHOR]

This study investigated the scientific map and thematic structure of Social media. It is a scientometrics research, using quantitative method and citation analysis. In order to draw a scientific map of the research literature in the field of Social media, the data were extracted from the Web of Science database within a specified time period. The retrieved records were formatted using WC10.exe conversion software and were then mapped using VOSviewer software. The findings revealed a growing trend in the number of articles published in the field of Social media, and this number increases on an annual basis. The most widely used subject areas in the field of Social media were identified. High‐frequency keywords such as Social media, education, sociology, social justice, women studies, and psychiatry are the newly emerging and active concepts in this field. Regarding the widely used subject areas in the field of Social media, it was noted that the two areas of Social media and education are ranked first and second as the most frequently used terms. In this study, by the conceptual map found in the field of Social media, it can be concluded that the concepts are highly interconnected. Furthermore, by the interconnectivity and closeness of centrality measures of social concepts illustrated in the provided map, it can be argued that the highest values are attributed to the subject areas of Social media and education. [ABSTRACT FROM AUTHOR]

This paper discusses the incorporation of supra-local social structure (SLSS) analysis into social impact assessment (SIA) practice in order to afford a deeper and more complex understanding of the social production of the impacts of planned interventions. We define SLSS as the total set of political, economic, socio-cultural and ideological driving forces and external structural phenomena shaping the social vulnerability of affected communities. We advocate causal network analysis for effectively incorporating SLSS into SIA and we take the conflict over the HydroAysén project in Chilean Patagonia as an empirical case study. While previous applications have interpreted planned interventions as the root cause of impacts, this paper analyses the dialectical interaction of four elements: the SLSS, the local community, the planned intervention and its impacts. This application revealed two fundamental issues. First, on a theoretical-conceptual level, it showed the capacity of SLSS to mould the causal pathways of a project's impacts on the affected community. Second, on an applied level, it enabled identification of the elements that should be addressed to facilitate social management of the project. • Technocratic practice of SIA has excluded the study of the sociostructural environment. • The SSE includes macro and meso driving forces acting on a supralocal scale. • Multicausal Impact Network Analysis allows for SSE incorporation into SIA. [ABSTRACT FROM AUTHOR]

The paper provides a theoretical model of technology adoption based on the idea that the diffusion of information about a technology depends both on the social structure of the adopters and their degree of assortativity. We propose a framework that - while retaining the core assumptions of epidemic diffusion models - allows for explicit modelling of the social structure via social network and of agents cultural heterogeneity via agent-based simulation. Decision-making takes place in institutional contexts where individual features trigger differentiated imitative responses and societal organization acts as medium on which information flows. The model simulates the diffusion of fertilizers in five Ethiopian villages (Peasant Associations), which differ in both political and relational structures and farmers belong to numerous ethnic and religious groups. Starting from survey data we run a compositional understanding simulation with the aim of reproducing observed diffusion curves on the basis of unobserved individual interactions. By minimizing the divergence from model output and observed diffusion, the exercise of categorical calibration and time series fit identify a set of plausible parameters for each village. Results highlight the importance of cultural dissimilarities to understand the diffusion processes. [ABSTRACT FROM AUTHOR]

The analysis of Social Networks widely is based on their respective graphical structures. Centrality measures of actors usually consider their position in a graph. Sometimes graphs are an insufficient medium to represent social structures. In this paper the authors propose a new framework with the purpose to analyze social fabric correctly: information theory. A set of conditionals forms the knowledge base about the network's structure. Once the knowledge base is adapted under maximum entropy or minimum cross-entropy a new form of analysis is available. The construction of knowledge bases and their analyses are realized in the expert system shell SPIRIT. [ABSTRACT FROM AUTHOR]

Burials provide valuable information to study social structures based on the assumption that burials and associated grave goods can represent social roles and relations in a society. To study social relationships, network analysis has been increasingly applied to archaeological data to infer interactions and relationships between entities. Statistical approaches to network analysis, such as exponential random graph models (ERGMs), provide a way to test hypotheses about dynamic processes of network formation. However, computational difficulties and sensitivity to uncertainties limit the application of ERGMs. In this paper, we introduce a Bayesian framework on ERGMs that enables an efficient computational process, effective quantification of uncertainty, and robust model evaluation of network properties. We tested a hypothesis of social change relative to the arrival of Europeans by studying burial data from Kiwulan, an Iron Age site in northeastern Taiwan. The results indicate a transition among the burials from network ties based on ritual objects to wealth objects, and a more centralized structure with increased social differentiation after the European presence was established in the 17th century. Our case study demonstrates the effectiveness of Bayesian network analysis for archaeological data, and expands the use of burials in understanding the impacts of colonial presence on Indigenous groups in a pericolonial context. • We present Bayesian inference on exponential random graph models (ERGMs) to model archaeological networks. • We explore social processes and network structures in archaeological data from two time periods. • Differences in network statistics imply change over time in social structures influenced by foreign presence. • Bayesian ERGMs are efficient and useful for evaluating and interpreting networks. [ABSTRACT FROM AUTHOR]

When a social species inhabits disparate environments with different requirements, it presents an ideal study framework for investigating plasticity in social structure. Common dolphins (Delphinus delphis) are wide‐ranging offshore delphinids that generally form societies with fission‐fusion dynamics within large schools and exhibit weak social bonds. In Port Phillip, southeastern Australia, common dolphins of the same species are, against expectations, resident to an embayment. Residency in this species provides a unique opportunity to investigate whether their social structure resembles that of their offshore conspecifics with weak social bonds, or whether bay living leads to stronger social bonds. We investigated the social structure of 12 resident adult common dolphins, between 2007 and 2014, in Port Phillip. Network analyses revealed nonrandom associations and several strong bonds, a social structure unusual for this species. The study shows that the social structure of a wide‐ranging gregarious species in Port Phillip reflects the requirements of a confined environment with limited but predictable resources. Their social structure in the bay resembles that of inshore delphinids, rather than of its own species. Our study highlights the extreme plasticity in social structure that common dolphins are capable of and the importance of the environment for social bonds. [ABSTRACT FROM AUTHOR]

We study a simple agent-based model of the recurring fashion cycles in the society that consists of two interacting communities: “snobs” and “followers” (or “opinion hunters”, hence the name of the model). Followers conform to all other individuals, whereas snobs conform only to their own group and anticonform to the other. The model allows to examine the role of the social structure, i.e. the influence of the number of inter-links between the two communities, as well as the role of the stability of links. The latter is accomplished by considering two versions of the same model—quenched (parameterized by fraction L of fixed inter-links) and annealed (parameterized by probability p that a given inter-link exists). Using Monte Carlo simulations and analytical treatment (the latter only for the annealed model), we show that there is a critical fraction of inter-links, above which recurring cycles occur. For p ≤ 0.5 we derive a relation between parameters L and p that allows to compare both models and show that the critical value of inter-connections, p*, is the same for both versions of the model (annealed and quenched) but the period of a fashion cycle is shorter for the quenched model. Near the critical point, the cycles are irregular and a change of fashion is difficult to predict. For the annealed model we also provide a deeper theoretical analysis. We conjecture on topological grounds that the so-called saddle node heteroclinic bifurcation appears at p*. For p ≥ 0.5 we show analytically the existence of the second critical value of p, for which the system undergoes Hopf’s bifurcation. [ABSTRACT FROM AUTHOR]

Extensive interdisciplinary literatures have built on the seminal spatial dilemmas model, which depicts the evolution of cooperation on regular lattices, with strategies propagating locally by relative fitness. In this model agents may cooperate with neighbors, paying an individual cost to enhance their collective welfare, or they may exploit cooperative neighbors and diminish collective welfare. Recent research has extended the model in numerous ways, incorporating behavioral noise, implementing other network topologies or adaptive networks, and employing alternative dynamics of replication. Although the underlying dilemma arises from two distinct dimensions—the gains for exploiting cooperative partners (Greed) and the cost of cooperating with exploitative partners (Fear)–most work following from the spatial dilemmas model has argued or assumed that the dilemma can be represented with a single parameter: This research has typically examined Greed or Fear in isolation, or a composite such as the K-index of Cooperation or the ratio of the benefit to cost of cooperation. We challenge this claim on theoretical grounds—showing that embedding interaction in networks generally leads Greed and Fear to have divergent, interactive, and highly nonlinear effects on cooperation at the macro level, even when individuals respond identically to Greed and Fear. Using computational experiments, we characterize both dynamic local behavior and long run outcomes across regions of this space. We also simulate interventions to investigate changes of Greed and Fear over time, showing how model behavior changes asymmetrically as boundaries in payoff space are crossed, leading some interventions to have irreversible effects on cooperation. We then replicate our experiments on inter-organizational network data derived from links through shared directors among 2,400 large US corporations, thus demonstrating our findings for Greed and Fear on a naturally-occurring network. In closing, we discuss implications of our main findings regarding Greed and Fear for the problem of cooperation on inter-organizational networks. [ABSTRACT FROM AUTHOR]

Teotihuacan was the first urban civilization of Mesoamerica and one of the largest of the ancient world. Following a tradition in archaeology to equate social complexity with centralized hierarchy, it is widely believed that the city’s origin and growth was controlled by a lineage of powerful individuals. However, much data is indicative of a government of co-rulers, and artistic traditions expressed an egalitarian ideology. Yet this alternative keeps being marginalized because the problems of collective action make it difficult to conceive how such a coalition could have functioned in principle. We therefore devised a mathematical model of the city’s hypothetical network of representatives as a formal proof of concept that widespread cooperation was realizable in a fully distributed manner. In the model, decisions become self-organized into globally optimal configurations even though local representatives behave and modify their relations in a rational and selfish manner. This self-optimization crucially depends on occasional communal interruptions of normal activity, and it is impeded when sections of the network are too independent. We relate these insights to theories about community-wide rituals at Teotihuacan and the city’s eventual disintegration. [ABSTRACT FROM AUTHOR]