Social Network Analysis Approach Research Articles

The Construction and Application of E-Learning Curricula Evaluation Metrics for Competency-Based Teacher Professional Development

Today, students at universities in advanced countries typically enroll in colleges, such as the College of Education, which offer interdisciplinary programs for undergraduates in their first and second years, allowing them to explore personal interests, experience educational research fields, complete their integrated curricula, and then choose a major in their third year. To cooperate with the government’s epidemic prevention policies and measures in the post-COVID-19 era, the trend of e-learning and distance teaching has accelerated the establishment of integrated online curricula with interdisciplinary programs for undergraduates in the College of Education to facilitate effective future teacher professional development (TPD). Therefore, it is very important to construct e-learning curricula evaluation metrics for competency-based teacher professional development (CB-TPD) and to implement them in teaching practice. This research used social network analysis (SNA) methods, approaches, and theoretical concepts, such as affiliation networks and bipartite graphs comprised of educational occupational titles and common professional competencies (i.e., Element Name and ID), as well as knowledge, skills, abilities, and other characteristics (KSAOs), from the U.S. occupational information network (O*NET) 26.1 OnLine database, to collect data on the occupations of educational professionals. This study also used Gephi network analysis and visualization software to carry out descriptive statistics of keyword co-occurrences to measure their centrality metrics, including weighted degree centrality, degree centrality, betweenness centrality, and closeness centrality, and to verify their importance and ranking in professional competency in eight categories of educational professionals (i.e., three categories of special education teachers and five categories of teachers, except special education). The analysis of the centrality metrics identified the educational common professional competency (ECPC) keyword co-occurrences, which were then used to design, develop, and apply e-learning curricula evaluation metrics for CB-TPD. The results of this study can be used as a reference for conducting related academic research and cultivating educational professionals’ online curricula, including ECPC keywords, integrated curricula design and the development of transdisciplinary programs, and teacher education, as well as to facilitate the construction and application of future e-learning curricula evaluation metrics for CB-TPD.

Towards expediting the implementation of sustainable and successful lean paradigm for construction projects: a hybrid of DEMATEL and SNA approach

PurposeProper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.Design/methodology/approachIn this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.FindingsConsidering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.Practical implicationsThe outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.Originality/valueThis paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.

Social Network Analysis: Mathematical Models for Understanding Professional Football in Game Critical Moments—An Exploratory Study

Considering the Social Network Analysis approach and based on the creation of mathematical models, the aim of this study is to analyze the players’ interactions of professional football teams in critical moments of the game. The sample consists in the analysis of a 2019/2020 season UEFA Champions League match. The mathematical models adopted in the analysis of the players (micro analysis) and the game (macro analysis) were obtained through the uPATO software. The results of the networks indicated a performance pattern trend more robust in terms of the mathematical model: Network Density. As far as it concerned, we found that the Centroid Players had a decisive role in the level of connectivity and interaction of the team. Regarding the main critical moments of the game, the results showed that these were preceded by periods of great instability, obtaining a differentiated performance in the following mathematical models: Centrality, Degree Centrality, Closeness Centrality, and Degree Prestige. We concluded that the networks approach, in concomitance with the dynamic properties of mathematical models, and the critical moments of the game, can help coaches to better evaluate the level of interaction and connectivity of their players toward the actions imposed by opponents.

The Emergence Process of Construction Project Resilience: A Social Network Analysis Approach

For construction projects, resilience is the process of resisting and recovering from adversity. With the global economic and social environment constantly changing, improving the resilience of construction projects has become a research hotspot in the field of project management. On the basis of social capital theory, this study constructs a construction project organization resilience evaluation system from two dimensions of bonding and bridging social capitals. Then, a new theoretical framework is proposed: the network dynamic evaluation model of project resilience based on the resource conservation strategy. Using survey data of 247 construction engineering practitioners, this study considers the emergence of organization resilience in the three phases of adversity. The results reveal that when the construction project is hit by adversity, the investment capital will increase but decrease in the recovery phase. Protective capital demonstrates the opposite. However, both types of capital finally reach a higher level than before the adversity, thus forming an emergence curve of project resilience. This study helps to understand the emergence process of the construction project resilience, provides a feasible method to calculate the resilience and social capital of construction projects in different phases of disasters, and improves the risk response ability of construction projects.

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

AbstractThe formation of an industrial network is inevitable because an industrial structure evolves to a higher level. The continuous material exchange among sectors creates a path for the embodied carbon in products to flow freely among industries. With the gradual complication of industrial networks, however, the emission reduction behavior of sectors not only becomes rooted in individual conduct, but also originates from the interaction forces between individuals. Therefore, carbon management in industries in the real economy needs to be explored from the perspective of these complex networks. On the basis of these considerations, this study utilized a hybrid model, the Economic Input–Output Life Cycle Assessment model, to construct the carbon footprint network of China's industries in 2002, 2007, 2012, and 2017. We also applied social network analysis to quantify the structural characteristics and evolutionary patterns of the network. Results showed that the transmission efficiency of resources and the subgroup‐integrated degree of the actors within the carbon footprint network were excellent, which was conducive to coordinated carbon emission reduction of these sectors from a network perspective. Agriculture and services were the engines driving sector‐wide carbon emission reduction through links within the network. Furthermore, some sectors, such as transportation and the warehousing industry, basic chemicals and chemical products manufacturing, and metal smelting and rolling processing industry, act as bridges and brokers. These sectors are keys to ensuring network cohesion and reducing the risk of network fragmentation. Finally, the industrial block network presented a hierarchical structure of a core–edge network. The network followed a trend of structural changes in which the network core became increasingly clear, and the network distribution pattern gradually concentrated on a single block. This finding revealed a new pathway for collaborative carbon mitigation in the industry from the perspective of the industrial community.

The ways social networks shape reflection on early significant clinical experiences in medical school.

BackgroundMedical curricula are increasingly providing opportunities to guide reflection for medical students. However, educational approaches are often limited to formalized classroom initiatives where reflection is prescriptive and measurable. There is paucity of literature that explores the personal ways students may experience authentic reflection outside of curricular time. The purpose of this study was to understand how social networks might shape dimensions of reflection.MethodsThis study employed a qualitative social network analysis approach with a core sample of seven first year undergraduate medical students who described their relationships with 61 individuals in their networks. Data consisted of participant generated sociograms and individual semi-structured interviews.ResultsMany learners struggled to find significant ways to involve their social networks outside of medicine in their new educational experiences. It appeared that some medical students began in-grouping, becoming more socially exclusive. Interestingly, participants emphasized how curricular opportunities such as reflective portfolio sessions were useful for capturing a diversity of perspectives.ConclusionsOur study is one of the first to characterize the social networks inside and outside of medical school that students utilize to discuss and reflect on early significant clinical experiences. Recent commentary in the literature has suggested reflection is diverse and personal in nature and our study offers empirical evidence to demonstrate this. Our insights emphasize the importance of moving from an instrumental approach to an authentic socially situated approach if we wish to cultivate reflective lifelong learning.

Twitter Networks during the Global COVID-19 Pandemic: Online Networking at the Time of Physical “Social Distancing”

This longitudinal study examined Twitter networks during the global COVID-19 pandemic in 2020 and 2021. Networks and content from of 87,793 users, 109,204 connections, and 1,655 unique tweets were explored in a multimethod approach of social network analysis and content analysis. The findings show that Twitter users focused on medical issues, politics, and blaming during the COVID-19 pandemic more than other topics and relied more on news and self-information than official sources.

Gender, care and kinships networks: Family forms in Santiago, Chile

This paper aims to understand how Chileans in Santiago define their families. This study used a mixed methods approach, combining qualitative and quantitative techniques in different stages of the research. First, it was done 16 in-deep-interviews, using name generators in ego-networks as a technique to process the data of the kinship networks of each person interviewed. Later, a metrical network analysis was done to describe the cognitive structures and the relations in the families using a mixed methods approach. The results show that families in Santiago can be described in four typologies: clique networks, cohesive families, families with extended and/or branched networks, and family centre-periphery networks. Furthermore, it concluded that the network families of women have a significant internal cohesion compared to the family networks described by men. In addition, the social network analysis approach was used to demonstrate diversity in the conformation of family forms, defined by the interviewees with their understanding of kinship structures.

Tunda Pilkada: Apakah Advokasi Digital Berhasil Memengaruhi Proses Pembuatan Kebijakan?

Digital advocacy is a growing and interesting area of research. This study aims to analyze the actors, processes, and effectiveness of digital advocacy regarding the postponement of the local elections in Indonesia in 2020 carried out by collective action using Twitter hashtags. Data in this study is the tweet related to the hashtag “Tunda Pilkada” from March 15 to December 8, 2020. The author argues that despite social media facilitating advocacy, not all digital advocacies provide effectiveness in influencing public policy. This study uses mixed research methods by combining a social network analysis approach to identify the actors. Hence, this study uses a qualitative content analysis approach to analyze social media-based advocacy models and identify failures in digital advocacy. The research findings found that individuals, not advocacy groups drove collective action. Furthermore, research also proves that hashtags are effective in reaching, building connections in public, and mobilizing. Finally, this study found that the failure of digital advocacy was caused by various factors that came from internal and external collective action. Our research contributes to the development of studies on digital advocacy.

Measuring the diffusion of conspiracy theories in digital information ecologies

Digital platforms and media are fertile breeding grounds for disinformation and conspirational views. They provide a variety of communication venues for a mixed set of actors and foster the diffusion of content between actor groups, across platforms and media, and across languages and geographical spaces. Understanding those diffusion processes requires approaches to measure the prevalence and spread of communicative acts within and across digital platforms. Given the increasing access to digital data, computational methods provide new possibilities to capture this spread and do justice to the interrelated nature and hybridity of online communication. Against this background, the paper focuses on the spread of conspiracy theories in digital information ecologies. It provides a review of recent methodological approaches to measuring conspiracy-related content online regarding the (a) prevalence and (b) diffusion of conspiracy theories. To that end, the paper differentiates between social network analysis approaches and computational techniques of automated text classification. It further discusses how far these and related computational approaches could facilitate studying the diffusion of conspiracy theories across different actor types, languages, topics and platforms. In doing so, it takes the specific nature of online communication and challenges in the field of conspiracy-related content into account.

Blockchain technology in the construction industry: mapping current research trends using social network analysis and clustering

Blockchain represents an evolving technology for distributed and secure recording and sharing of information. Meanwhile, blockchain has thrived in banking, finance, and supply chain; its usage within the construction industry is still in its infancy. To this end, the existing literature falls short in providing comprehensive quantitative understanding, within a systems-based analytic context, of the factors affecting blockchain utilization in construction applications. This paper fills this knowledge gap. The authors: (1) conducted an extensive literature review on blockchain implementation in the construction domain; (2) identified a list of 41 factors affecting blockchain implementation in construction projects categorized in four categories: challenges, needs, requirements, and capabilities; (3) utilized a social network analysis (SNA) approach on a database of 111 publications to quantitatively analyze the literature as related to the aforementioned factors; and (4) performed clustering analysis on the SNA graphs to determine the combinations of factors that are most likely co-occurring in research publications. SNA results indicate that while the most investigated factor was “increased trust and transparency between project parties”, the least studied factors included: “cash upfront funding system”, “change payment processes and procedures”, “smart contracts design errors”, “cryptocurrency fluctuations”, “lack of sufficiently skilled personnel”, and “increased awareness and capabilities of personnel”. Also, clustering outcomes highlight that some combinations of factors are not well-represented in current scholarly efforts. Such imbalance and consequent knowledge gaps may contribute to the actual implementation rate of blockchain in construction applications. Ultimately, this paper provides a roadmap for potential future directions of blockchain construction-related research.

#overtourism on Twitter: a social movement for change or an echo chamber?

ABSTRACT When the linguistic innovation and phrase overtourism was used in an online news report to describe excessive tourism in Iceland in 2016, legacy and social media soon following with in-depth articles and visual representations of perceived excessive tourism in other locations around the world. Given the growing calls for action on overtourism, this study takes a social network analysis (SNA) approach, using a network analysis and visualization software package called NodeXL Pro, to better understand the 10,325 tweets which used the hashtag ‘overtourism’ between July 2013 and September 2020. By exploring central users, conversation starters, gatekeepers and influencers, the analysis indicates that an ad hoc network was built around #overtourism on twitter. The analysis indicates that this network is held together by a small number of experts, who play a key role in presenting, distributing and circulating information about this controversial topic. While the studies practical contribution is the use of NodeXL Pro for advanced social media network analysis, the findings also indicate that the ability of these experts to influence perceptions of overtourism inside and outside twitter will depend on whether it can engage broader publics as the tourism sector recovers from a global pandemic.

A Decade of Activity of the Medical Journal of Tabriz University of Medical Sciences through Scientometric Method (2010-2019)

A Decade of Activity of the Medical Journal of Tabriz University of Medical Sciences through Scientometric Method (2010-2019)

The Power of School Conditions: Individual, Relational, and Organizational Influences on Educator Wellbeing.

Wellbeing in schools is often focused at the individual level, exploring students’ or teachers’ individual traits, habits, or actions that influence wellbeing. However, studies rarely take a whole-school approach that includes staff wellbeing, and frequently ignore relational and organizational level variables. We take a systems informed positive psychology approach and argue that it is essential to build greater understanding about organizational and relational influences on wellbeing in order for schools to support educator wellbeing. Our study evaluated the relative contributions of individual, relational, and organizational factors to educator wellbeing. Our measure of wellbeing focused on the life satisfaction and flourishing of 559 educators in 12 New Zealand schools. We used a social network analysis approach to capture educators’ relational ties, and demographic data and psychometric scales to capture individual and organizational level variables. Results of hierarchical blockwise regressions showed that individual, relational, and organizational factors were all significantly associated with educator wellbeing; however, it was educators’ perceptions of trusting and collaborative school conditions that were most strongly associated with their wellbeing. The number of relational ties educators had explained the least amount of variance in wellbeing. Educators were more likely to experience high levels of support when their close contacts also experienced high levels of support. However, for many educators, there was a negative association between their most frequent relational ties and their reported levels of support. Our results suggest that attending to the organizational factors that influence wellbeing, through creating trusting and collaborative school conditions, may be one of the most influential approaches to enhancing educator wellbeing. We call for whole-school approaches to wellbeing that not only consider how to support and enhance the wellbeing of school staff as well as students, but also view the conditions created within a school as a key driver of wellbeing within schools.

Pemanfaatan Social Network Analysis (SNA) Untuk Analisis Pemetaan Komunikasi Pada Pegawai Bank Syariah di Jawa Tengah

Bank Syariah Mandiri is one of the sharia business units which is a work unit of the main office (parent) of conventional commercial banks which has a function as the main office of the sharia unit office or sharia branch. So far, the performance of Islamic banks is only seen from the number of assets and customers, but very rarely describes the internal conditions related to the performance of their employees. Good communication and coordination between departments is very important so that the company can run effectively and efficiently. The purpose of this research is that the company is expected to be able to analyze employee interactions through communication patterns and become policy advice in decision making, especially related to HR development. This study involved approximately 20 employees at a branch of Bank Syariah Mandiri in Central Java Province which used a social network analysis (SNA) approach. In the SNA approach, the values of density, degree centrality, closeness centrality, betweenness centrality, clicks, and visualization of relationships using a sociogram will be sought. As a result, all inter-departmental employees have good communication relationships. Based on these findings, it is necessary to solve the problem by making actors or employees who have high indegree scores to be more proactive in providing information to their colleagues so that coordination can be smooth.

Bonding Social Capital of Rural Women in Southwest Iran: Application of Social Network Analysis*

AbstractThe establishment of local communal organizations is a unique strategy of rural communities to build resilience against economic challenges. Shirvareh is traditionally recognized as an important communal organization of women in Southwestern Iran for managing dairy production and securing their livelihood. Preserving this vital social institution and its associated indigenous knowledge was the main motivation of this study to examine factors influencing Shirvareh’s performance. Given that relevant studies on the performance of this social organization in Iran are rare, the current study was an attempt to fill this gap through an empirical study using a social network analysis (SNA) approach. The general goal of this study was to investigate the networks of trusting relationships of rural women involved in the Shirvareh organization in Ghasr‐e‐Yaghoub village located in Fars province, Southwestern Iran. The results revealed that possible in‐group trust ties were maximum whereas out‐group trust ties were moderate to high, meaning that bonding social capital at individual, sub‐group, and network level are generally high. High in‐group and out‐group ties enable community members to cope with disturbances, initiate social innovation, and act collectively, which in this case, assures the sustainability of their efficient traditional organization and protects them against poverty.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis.

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

Institutional analysis of organizations active in the restoration of Lake Urmia: the application of the social network analysis approach

ABSTRACT This study aimed to analyse, using the social network analysis approach, the institutional organizations active in the restoration of Lake Urmia regarding the issues related to credit and management in the provinces of East Azerbaijan and West Azerbaijan. Forty relevant institutional organizations were identified and their communications and interactions were analysed using expert and key information. According to the results, the density index is low in the networks of organizations involved in issues related to credit and management. Results pertaining to issues of credit in the studied network revealed that certain institutions (Urmia Lake Restoration Program, Department of Natural Resources and Watershed of West Azerbaijan Province, Keshavarzi Bank and Urmia Lake Restoration Program, West Azerbaijan Province) had a higher impact (higher betweenness centrality) on others, but different institutions (Urmia Lake Restoration Program, educational and academic centres, West Azerbaijan Province Meteorological Department, and Department of Natural Resources and Watershed of West Azerbaijan Province) had a higher impact (higher betweenness centrality) on other institutions concerning issues related to management.

A Citizen Science Community of Practice: Relational Patterns Contributing to Shared Practice

Citizen science networks are a recent global phenomenon, with associated communities of practice that have emerged to support growth in the field and the development of practices. Effective communities of practice are dependent on the interactions from the social network underpinning the community. We examined the Australian citizen science practitioner network, using a combined social network analysis and survey approach. Our goal was to understand the structure and characteristics of this network, to establish who participates in this network, where and how interactions occur, and explore what participation achieves for the users. The Australian citizen science practitioner network has benefited from face-to-face citizen science events to make important connections that have been leveraged to benefit other working relationships and positive outcomes, especially for early-career practitioners and women within the network. How the community of practice continues to navigate successful knowledge exchange across society and science, whether through interactions in face-to-face or virtual settings, will need to be addressed as the community continues to grow in scope and size. In particular, the network will need to consider supporting key individuals who play important bridging functions across the citizen science practitioner network. The emergence of transdisciplinarity amongst those working in citizen science is a promising property of this learning community that is worth working strategically to maintain.

Reprint of: Using social network analysis to study crime: Navigating the challenges of criminal justice records

The use of social network analysis to study groups of offenders engaged in illicit activities such as drug trafficking and terrorism has grown in popularity over the last three decades. Along with such growth, however, researchers have been confronted with a suite of challenges related to the use of data extracted from criminal justice records. In this paper, we review these challenges through a discussion of the extant empirical literature utilizing social network analysis approaches that draw data from the criminal justice system. First, we outline and discuss the different types of data used across this literature. Second, we chronicle the challenges that have emerged across the field of criminal networks via a comprehensive review of the literature. In particular, we draw on the documented experiences of researchers in the field, including our own, and detail “archeological” approaches that future researchers can utilize to adapt and overcome said challenges. The use of criminal justice records can suffer from a number of limitations, mainly with respect to accuracy, validity and reliability. Such data may include errors, both intentional (e.g. aliases, false information) and unintentional (e.g. transcription errors), including missing data. The use of criminal justice records present particular problems with defining the network boundary as the boundary as determined by law enforcement or prosecution agencies may not correspond to the boundary as defined by network members. We conclude by offering a number of recommendations for researchers about data collection and preparation when utilizing criminal justice records.