Social Network Characteristics Research Articles

Team leadership and leadership roles

Although leadership theory recognises the importance of leaders in a team, research has mostly focused on various personal characteristics of leaders or leadership style, while less attention has been paid to the social relationships in which leaders are embedded. We therefore examine both internal and external social ties of leaders with the aim of identifying variations in the network patterns of team leaders. We hypothesise that leaders' relationship patterns lead to different network structures, which in turn create differences in leadership roles. By examining the social networks of team leaders from Slovenian and Finnish organisations between 2009 and 2012, we empirically identify variations in leaders' social networks. The results suggest four distinct social structures within which team leaders perform their leadership roles. We have shown that leadership roles can be distinguished from teams and team leaders based on the characteristics of social networks.

Backend Development of a Microservice-Based Website Application for Public Issue Reporting: Case Studyn in People Representative Council

This research introduces a web application aimed at advancing public issue reporting, promoting civic engagement, and expediting government responses. In collaboration with DPRD Jawa Barat, the research leverages Scrum methodology, social network features, and a microservice architecture to create an efficient communication platform between citizens and governmental bodies. The backend of the application, developed using the Go programming language, adopts a microservice architecture to enhance scalability and maintainability. The Scrum methodology facilitates an agile development process, ensuring adaptability to changing requirements and fostering continuous improvement throughout the project lifecycle. Additionally, the study explores the incorporation of social network features to encourage public engagement within the application. This integration allows citizens to connect, share, and discuss public issues, further enhancing the collaborative nature of the reporting platform. To ensure the seamless functionality of the microservices, API testing is employed, validating the reliability and consistency of the application's interfaces. Stress testing is also conducted to assess scalability and performance capabilities, identifying potential optimizations for the system's responsiveness under varying levels of load. In conclusion, this research presents an innovative solution for public issue reporting that combines microservice architecture, Scrum methodology, and social network features. The application's integration of these elements aims to not only streamline citizen-government communication but also create a dynamic platform that encourages active public involvement and collaboration.

Social Network Characteristics Associated with More Frequent HIV and STI Prevention Conversations: The N2 Cohort Study in Chicago.

Black cisgender sexually minoritized men (SMM) and transgender women (TW) are subgroups at highest risk of HIV and sexually transmitted infections (STIs) in the US. We sought to identify factors facilitating continued conversations - social reinforcement - surrounding HIV/STI prevention among this subgroup. Participants were recruited in Chicago from 2018 to 2019 from community health spaces. Participants provided information about themselves (level 2) and ⩽5 confidants (level 1). We used multinomial multilevel modeling to identify associations with HIV/STI prevention conversation frequency. A total of 370 participants provided information on 987 confidants (mean = 2.6). We found significantly positive associations between having biweekly or more often HIV/STI prevention conversations and a confidant being a kin family member, older by 15 years or more, racially homophilous, and emotionally close. Future interventions should harness social networks by including components that consider racial homophily, respect for elders, and strong ties, in addition to applying kin family systems interventions approaches and decreasing stigma surrounding HIV/STIs.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.

Analyzing the Impact of Innovative Features in New Social Networks on User Engagement and Content Sharing Dynamics

This study investigates the effects of novel functionalities on user behavior across emerging social media platforms. Employing a mixed-methods research design, the study integrates quantitative analysis of engagement metrics with qualitative feedback from user interviews and surveys to assess how augmented reality (AR) filters, ephemeral content, and algorithmic curation influence engagement and content sharing. Results indicate these features significantly enhance user interaction, suggesting that innovation is key to user retention and active participation. Additionally, the study examines the double-edged nature of algorithmic curation, balancing user experience personalization against the risks of creating informational echo chambers. The implications of these findings extend to various stakeholders in the digital ecosystem, including content creators, marketers, technology developers, and policymakers, emphasizing the importance of user-centric innovation and ethical considerations in platform development. This research contributes to the broader understanding of social media evolution and its impact on digital communication practices.

Networks and Influencers in Online Propaganda Events: A Comparative Study of Three Cases in India

The structure and mechanics of organized outreach around certain issues, such as in propaganda networks, is constantly evolving on social media. We collect tweets on two propaganda events and one non-propaganda event with varying degrees of organized messaging. We then perform a comparative analysis of the user and network characteristics of social media networks around these events and find clearly distinguishable traits across events. We find that influential entities like prominent politicians, digital influencers, and mainstream media prefer to engage more with social media events with lesser degree of propaganda while avoiding events with high degree of propaganda, which are mostly sustained by lesser known but dedicated micro-influencers. We also find that network communities of events with high degree of propaganda are significantly centralized with respect to the influence exercised by their leaders. The methods and findings of this study can pave the way for modeling and early detection of other propaganda events, using their user and community characteristics.

Examining the Role of Structural and Functional Social Network Characteristics in the Context of Chronic Pain: An Ego-centered Network Design

The well-being and functioning of individuals with chronic pain (CP) vary significantly. Social factors, such as social integration, may help explain this differential impact. Specifically, structural (network size, density) as well as functional (perceived social support, conflict) social network characteristics may play a role. However, it is not yet clear whether and how these variables are associated with each other. Objectives were to examine 1) both social network characteristics in individuals with primary and secondary CP, 2) the association between structural network characteristics and mental distress and functioning/participation in daily life, and 3) whether the network’s functionality mediated the association between structural network characteristics and mental distress, respectively, functioning/participation in daily life. Using an online ego-centered social network tool, cross-sectional data were collected from 303 individuals with CP (81.85% women). No significant differences between individuals with fibromyalgia versus secondary CP were found regarding network size and density. In contrast, ANCOVA models showed lower levels of perceived social support and higher levels of conflict in primary (vs secondary) CP. Structural equation models showed that 1) larger network size indirectly predicted lower mental distress via lower levels of conflict; 2) higher network density increased mental distress via the increase of conflict levels. Network size or density did not (in)directly predict functioning/participation in daily life. The findings highlight that the role of conflict, in addition to support, should not be underestimated as a mediator for mental well-being. Research on explanatory mechanisms for associations between the network’s structure, functionality, and well-being is warranted. PerspectiveThis paper presents results on associations between structural (network size, density) and functional (social support, conflict) social network characteristics and well-being in the context of CP by making use of an ego-centered network design. Results suggest an indirect association between structural network characteristics and individuals with CP their mental well-being.

A Survey: Fake News Detection Mobile App Using Machine Learning

Abstract: With the proliferation of digital information, the challenge of identifying and mitigating the spread of fake news has become increasingly crucial. This research paper provides a comprehensive review of existing technologies designed for the detection of fake news, with a particular focus on their adaptation to mobile applications. The study delves into various approaches, including natural language processing, machine learning algorithms, and deep learning models, highlighting their strengths and limitations. Additionally, it explores the integration of user-generated content analysis and social network features in enhancing the accuracy of fake news detection on mobile platforms. By synthesizing insights from recent advancements, this paper aims to inform the development of an effective mobile app for mitigating the impact of fake news in the digital landscape.

Social Media and Affordable Internet Marketing for Small Businesses

The article examines the features of social networks as an accessible marketing tool for small businesses. The current directions of marketing in social networks are specified and the features of such marketing are highlighted. Various tools are compared and their applicability in the context of small business Internet marketing is assessed. The potential advantages of marketing in social networks are clarified, associated with speed of response, relatively low costs, lack of focus on a local audience, etc. Marketing strategies in social networks are defined and disclosed, and issues of their development are addressed. Based on the results of the study, conclusions are drawn about the resulting importance of small business marketing on social networks.

Graph-aware multi-feature interacting network for explainable rumor detection on social network

At present, rumors are growing wantonly with the convenience and influence of social media, becoming a problem that may severely impact social stability and development. The rumor is not an objective judgment but a process of multi-dimensional subjective value superposition and a collective transaction of people’s thoughts on social networks. How to fully mine the critical features of rumor detection from the complex information of social networks is a challenge to the existing rumor detection models. Therefore, we present the explainable model GMIN (Graph-aware Multi-feature Interacting Network), aiming to fully exploit the multifaceted features of social networks by deeply analyzing the mechanism of rumor propagation and the nature of social networks. GMIN incorporates four modules to capture: the characteristics of people receiving and spreading information, the interactions and latent associations in social networks, the mechanisms of rumor propagation and diffusion, and the collaborations between various features. It is worth noting that GMIN is interpretable in the results and the model’s components. The experimental results on three real-world datasets demonstrate the validity of our proposed model and show excellent capabilities in early rumor detection and the interpretability of the model.

Ninety years after Lewin: The role of familism and attachment style in social networks characteristics across 21 nations/areas

Drawing on the literature on person-culture fit, we investigated how culture (assessed as national-level familism), personality (tapped by attachment styles) and their interactions predicted social network characteristics in 21 nations/areas ( N = 2977). Multilevel mixed modeling showed that familism predicted smaller network size but greater density, tie strength, and multiplexity. Attachment avoidance predicted smaller network size, and lower density, tie strength, and multiplexity. Attachment anxiety was related to lower density and tie strength. Familism enhanced avoidance’s association with network size and reduced its association with density, tie strength, and multiplexity. Familism also enhanced anxiety’s association with network size, tie strength, and multiplexity. These findings contribute to theory building on attachment and culture, highlight the significance of culture by personality interaction for the understanding of social networks, and call attention to the importance of sampling multiple countries.

Identifying Social Network Characteristics Associated With Youth Physical Activity Skill Competency at a Summer Care Program.

While literature suggests that youth physical activity (PA) behaviors are affected by network influences, less is known about network influences on perceived skill competency, a component of physical literacy and self-efficacy. This study aims to provide an understanding of potential network characteristics which are associated with youth PA skill competency. Youth (n = 158) between the ages of 8 and 12 years recruited from two summer care programs (i.e., Boys & Girls Clubs) participated in researcher-administered surveys. Youth self-reported their age, sex, involvement in team sports, weekly PA, and skill competency assessed using a version of the PLAYself measure. Youth were also asked to report up to five people in the summer program and five out of the program with whom they interacted the most. Linear regression was used to evaluate associations between skill competency and demographics, PA, and social network characteristics. Skill competency was significantly associated (R2 = .17) with age (β = -.06, p = .01), sex (β = -.06, p = .01), sports team involvement (β = .16, p < .001), and weekly PA (β = -.20, p < .001). Skill competency was also significantly associated with the number of connections with whom the youth played frequently (β = .09, p < .001), the number of connections that helped the youth to be active (β = .18, p < .001), and the heterogeneity of the type of relationships within the youth's network (β = .11, p < .001). Skill competency scores were significantly associated with both individual variables and social network composition. In addition, youth with networks comprising several types of relationships (heterogeneity) reported a significantly high skill competency. PA interventions can be most effective when considering the reinforcing aspects of networks and skill competency.

Development and usability testing of a cognitive-behavioral therapy-guided self-help mobile app and social media group for the post-acute treatment of anorexia nervosa

ObjectiveAnorexia nervosa (AN) is often treated in the acute setting, but relapse after treatment is common. Cognitive-behavioral therapy (CBT) is useful in the post-acute period, but access to trained providers is limited. Social support is also critical during this period. This study utilized a user-centered design approach to develop and evaluate the usability of a CBT-based mobile app and social networking component for post-acute AN support. MethodParticipants (N = 19) were recently discharged from acute treatment for AN. Usability testing of the intervention was conducted over three cycles; assessments included the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), the Mobile Application Rating Scale (MARS), a social media questionnaire, and a semi-structured interview. ResultsInterview feedback detailed aspects of the app that participants enjoyed and those needing improvement. Feedback converged on three themes: Logistical App Feedback, boosting recovery, and Real-World App/Social Media Use. USE and MARS scores were above average and SUS scores were “good” to “excellent” across cycles. ConclusionThis study provides evidence of feasibility and acceptability of an app and social networking feature for post-acute care of AN. The intervention has potential for offering scalable support for individuals with AN in the high-risk period following discharge from acute care.

Social network analysis and educational change: unravelling the role of innovative teaching staff in a higher education environment

ABSTRACT As the higher education landscape has shifted and calls for change and improvements have grown louder, researchers have turned to empirical investigations of what makes change possible in higher education. Recent studies on the impact of informal interactions for promoting change have given impetus to social network analysis as an approach to studying higher education change. However, the relationship between teachers’ social network characteristics and their disposition to adopt changes remains largely unknown. In this study, we describe the characteristics of teaching networks and explain the network characteristics of the most innovative teaching staff. By capturing whole teaching and research networks within a STEM discipline department at a Norwegian university, we show that teachers tend to speak to the same colleagues about both research and teaching, and that teaching communities are constrained by departmental sub-units (i.e. research groups). Furthermore, by combining the network data with a survey on diffusion of innovation, we find that the most innovative teachers (1) are central in the network, having the largest personal networks, (2) have a substantial reach to the rest of the network, and (3) are highly interconnected. This paper contributes to the understanding of change theories in higher education and discusses how these results can inform the practice of change agents and educational developers. In addition to confirming the presence of a research-teaching nexus, we discuss why leveraging the networks of the most innovative teaching staff would be a promising strategy for facilitating change in higher education.

Perceived social support and characteristics of social networks of families with children with special healthcare needs following the COVID-19 pandemic.

Children with special healthcare needs (CSHCN) require more support than the average of their peers. Support systems for CSHCN were particularly affected by pandemic control measures. Perceived social support is a resource for health and wellbeing for CSHCN and their families. Associations of social support, mental health and socioeconomic status (SES) have been described. This study aims to (1) assess perceived social support in families with and without CSHCN; (2) describe structure and types of social networks of families with and without CSHCN; and (3) explore associations between perceived social support, disease complexity, child and caregiver mental health, and SES. This is the third of a sequential series of cross-sectional online surveys conducted among caregivers of children ≤ 18 years in Germany since the beginning of the COVID-19 pandemic, administered between 1st December 2022 and 10 March 2023. The Brief Social Support Scale (BS6) assessed perceived social support. Child and parental mental health were assessed using the Strengths and Difficulties Questionnaire (SDQ) and WHO-5 Wellbeing index. The CSHCN-Screener identified CSHCN. Descriptive statistics and linear regression modeling assessed associations between perceived social support, parent-reported child mental health problems, disease complexity, caregiver mental wellbeing and SES. The final sample included 381 participants, among them 76.6% (n = 292) CSHCN. 46.2% (n = 176) of caregivers reported moderate, i.e., at least occasional social support. Social support was largely provided by informal social networks consisting of partners, relatives and neighbors/friends. Linear regression modeling revealed associations of lower perceived social support with higher disease complexity of the child, lower caregiver mental wellbeing, lower SES and increasing caregiver age. The results of this study describe inequalities in perceived social support according to disease complexity of the child, caregiver mental health and socioeconomic status. They highlight the importance of social support and support networks as a resource for wellbeing of caregivers and CSHCN. Moving on from the COVID-19 pandemic, recovery strategies should focus on low-threshold interventions based in the community to improve social support for families with CSHCN and actively involve caregivers in identifying needs and co-creating new approaches.

The moderating role of social capital for late‐career management intervention effects on older employees' work engagement

AbstractDespite increasing interest in supporting older workers' motivation, retention, and well‐being at work, knowledge about how social networks at work may affect the efficacy of training interventions among older employees is scarce. These social ties are an important source of resources for older workers' careers. This study examined the characteristics of older workers' personal social networks as boundary conditions for the effects of late‐career management intervention on work engagement. Data were used from an earlier randomized controlled trial (RCT) in which senior employees (mean age of 58 years) participated in a peer group‐based training intervention and were asked to complete follow‐up surveys at baseline, post‐intervention, and after 6 months (Vuori et al., 2019, Journal of Vocational Behavior, 115: 103327). The results showed that older workers' social ties at higher organizational levels (upper reachability) and the number of social ties at work moderated the effect of the intervention on work engagement. Specifically, the intervention aimed at enhancing employees' personal resources improved work engagement for senior employees with few or no social ties at work with whom they could discuss important matters, and for those with social ties at higher organizational levels. However, the relationship quality between older workers and their leaders showed no moderation effect. This study encourages human resources professionals to consider the social network characteristics and peer learning of older workers when providing training to enhance their work engagement.

Vulnerability or Resiliency? A Two-Wave Panel Analysis of Social Network Factors Associated with Glycemic Levels among Mexican Immigrants in the Bronx, NYC, Before and During COVID-19.

Latinos have high rates of type 2 diabetes mellitus (T2DM) yet are characterized as having health-promoting social networks. The impacts of COVID-19 on personal networks were complex, especially in urban areas with high proportion of immigrants such as the Bronx in NYC. Our objective was to test the extent to which network characteristics increase vulnerability or resiliency for glycemic control based on data gathered from Mexican-origin Bronx dwellers. We used two-wave panel study analyzing self-reported personal social networks (n=30participants; 600network members) and HbA1c levels via dried blood spots in 2019, before the COVID-19 pandemic, and in 2021, a time after initial lockdowns and when the pandemic was still ravaging the community of study. Regression models adjusted for individual-level variables including sociodemographic and health indicators (i.e., physical health including COVID-19 and mental health). We found that an increase in the proportion of network members with diabetes predicted an increase in participant's HbA1c levels from 2019 to 2021 (β=0.044, p < 0.05). Also, a greater proportion of network members consuming "an American diet" in 2019 predicted a decrease in participant's HbA1c levels (β=-0.028, p < 0.01), while a greater proportion of network members that encouraged participants' health in 2019 predicted an increase in participant's HbA1c levels (β=0.033, p < 0.05). Our study sheds light on specific social network characteristics relevant to individual diabetes outcomes, including potential longitudinal mechanistic effects that played out at the peak of the COVID-19 crisis.

Abstract TP258: Social Network Features Involved in Stroke Decision Making During Acute Stroke

Introduction: 80% of strokes occur in the company of others and 70% take place at home. However, research on prehospital delay has overlooked the social nature of stroke detection and triage. In the Time is Brain study, we aim to understand how a patient’s social network affects the decision to seek medical care during stroke symptom onset. Methods: We conducted in-depth interview in 78 patients (ages 30 to 90 years) in the longitudinal, multicenter Time is Brain study. During the interview, we mapped and examined relationships and interactions between patients and individuals involved in the decision-making process during the stroke. We enrolled during their initial stroke hospitalization at Mass General Brigham or Yale New Haven Hospital. Eligibility criteria include having an acute stroke syndrome presentation that may include diagnoses of acute ischemic stroke, intracerebral hemorrhage (ICH), or transient ischemic attack (TIA). Trauma-related syndromes, such as subdural hemorrhage, were excluded. Results: 82.5% (n=66) of patients were accompanied by at least one other person during symptom onset. 60% (n=48) contacted at least one other person. 82.5% (n=66) of strokes occurred in a home-setting and 17.5% (n=14) occurred at work or another public space. 36.25% (n=29) of patients identified their spouse as the first person made aware of symptoms. Men were more likely than women to be accompanied by a spouse (X 2 = 6.8211, p-value = 0.03302). Conclusions: Building on prior studies that have highlighted the important role of the social environment in stroke detection and triage, this is a high-resolution study of the social agents and behaviors driving stroke decision-making. These findings highlight the active and often lengthy communication, and unspoken observations that shape layperson decisions during stroke crises. These data are important to identify mechanisms of prehospital delay and build social network-based interventions to improve time-to-treatment.

SOCIO-PEDAGOGICAL LEVEL STRATIFICATION: PORTRAIT OF A VIRTUAL PERSONALITY

The authors tried to determine the psychological and pedagogical characteristics of the phenomenon of “virtual personality” in this article. The main purpose of this work is to show the results of the content analysis of socio- psychological reasons (anonymity and confidentiality, expression, and self-expression, research of roles and identities, social interaction and acceptance) for designing a portrait of a virtual personality in an “online community”. The quintessence of modeling the portrait of a virtual personality in the “online community” provides for the identification of a methodological concept for the analysis of socio-pedagogical stratification, applicable to establish a correlation between indicators of network communicative culture and behavioral patterns of Kazakhstani students. The quintessence of modeling the portrait of a virtual personality in the “online community” provides for the identification of a methodological concept for the analysis of socio-pedagogical stratification, applicable to establish a correlation between indicators of network communicative culture and behavioral patterns of Kazakhstani students. The results of the conducted research are presented, and the concepts are clarified: “virtual personality”, “virtual personality image”, and “virtual identity”. The algorithm of experimental research developed by us will allow: us to substantiate the deterministic features of social networks; reveal the dominant role of “online community” in the socialization of personality; to reveal its real influence on the value orientations of modern students. Keywords: online community, virtual personality image, virtual personality, virtual space, social networks, virtual self, virtual reality, virtual identity.