Social Network Characteristics Research Articles

EFFECT OF HEARING INTERVENTION ON LONELINESS AND SOCIAL ISOLATION IN THE ACHIEVE STUDY

Abstract Social health interventions in older adults typically have small effect and limited large-scale implementation. Treating hearing loss is a potential intervention, but efficacy is unknown. This study presents baseline associations between hearing loss, loneliness, and social isolation as well as the 3-year effect of a hearing intervention on social health. The Aging and Cognitive Health Evaluation in Elders Study (ACHIEVE) randomized controlled trial (n=977) tests the effect of hearing intervention (provision of hearing aids/related technologies, counseling/education) vs. successful aging health education control intervention on loneliness (UCLA Loneliness Scale) and social network characteristics (Cohen Social Network Index) (pre-specified exploratory outcomes) among older adults with untreated hearing loss. Hearing loss was quantified by better ear, speech-frequency pure tone average (PTA). Speech-in-noise recognition and hearing-related quality of life were also measured. Baseline associations between hearing loss and social health outcomes are assessed by robust Poisson regression adjusted for demographic and health characteristics. Three-year hearing intervention effects are assessed by generalized linear mixed effects models. Participants were 70-84 years, 54% female, and 87% White. At baseline, better speech-in-noise recognition was associated with greater social network characteristics (e.g., larger social network size [IRR: 1.04, 95% CI: 1.00, 1.07]). Worse hearing-related quality of life was associated with higher prevalence of loneliness (PR: 1.15, 95% CI: 1.11, 1.20) and smaller social network size (IRR: 0.96, 95% CI: 0.93, 1.00). Hearing intervention effect results will be available prior to the conference (data collection ends early 2023). Hearing treatment could be an effective social health intervention with greater scalability.

THE IMPACT OF CHANGES IN COGNITIVE FUNCTIONING ON SOCIAL ISOLATION

Abstract This study examines if transitioning from normal to impaired cognitive functioning is associated with changes in social isolation among community-dwelling older adults without cognitive impairment at baseline. This study uses nine waves (2011-2019) of data from the National Health and Aging Trends Study (NHATS). The sample (N=8,245) was representative of Medicare beneficiary at baseline. Social isolation was measured using a seven-item index that captures a range of social activities and network characteristics. Cognitive status was assessed through self-report of having Alzheimer’s disease, the Dementia Screening Interview (AD8), and cognitive testing. Based on pre-determined criteria, participants were grouped as either cognitively intact or impaired. Fixed effect linear modeling with robust standard errors was used to examine the impact of transitioning from intact to impaired cognitive functioning on social isolation. At baseline 5,575 Participants were cognitively intact. At each successive wave, between 12% and 16% of the sample was cognitively impaired. After controlling for time-varying covariates, transitioning from intact to cognitive impairment was associated with a statistically significant increase in social isolation (β=.18, SE=.03, p <.001). Gender and educational attainment did not impact this association. These findings underscore the need for policies and interventions to increase social connection among older adults, particularly during early stages of cognitive impairment.

The Role of Social Networks on Depression and Anxiety Among a Sample of Urban American Indian/Alaska Native Emerging Adults

PurposeMental health inequalities continue to persist among American Indian/Alaska Native (AI/AN) people. However, few studies have examined the association of social networks and depression and anxiety among urban emerging AI/AN adults. MethodsThis study analyzes the association of social network characteristics with depression and anxiety among a sample of urban AI/AN emerging adults. A second set of regression models tested the same associations but controlling for respondent sexual and gender minority (SGM) status. Data were from a sample of 150 AI/AN emerging adults residing in urban areas from 20 different states (86% female; mean age 21.8; 48.0% SGM) who participated in a randomized controlled trial analyzing the effects of culturally grounded interventions on alcohol and other drug use and cultural connectedness. ResultsParticipants with a higher proportion of network members who were around the same age reported significantly less anxiety. Those who had a higher proportion of network members who they sometimes/often argue/fight with were more likely to report greater depression and anxiety. Participants with higher proportions of social network members who have ever lived on a reservation/Rancheria/tribal land/tribal village reported significantly less depression. However, participants with higher proportions of social network members who lived 50 miles away or more reported significantly more depression. Controlling for SGM status, results were largely similar. DiscussionResults highlight the role of social connections on the mental well-being of urban AI/AN emerging adults.

The Influence of Short Videos on College Students Purchasing Behavior

Short video has gradually developed into one of the practical expression ways of information content communication in the mobile Internetvarious social media and e-commerce use opportunities from this trend. College students are the most active and mainstream group in Short video. The fragmented communication mode, exciting content, and business behavior of short videos have been integrated into the group of college students, influencing them all the time, including but not limited to purchasing behavior. In this paper, college students are selected to build a mechanism model of the influence of word-of-mouth characteristics of social e-commerce networks on college students purchase intention, and the model is tested through empirical research. The results showed that when brand marketing is enhanced, brand interaction, experience perception, interest, and perception are enhanced. The more frequently consumers interact with the brand, the more frequently they connect and communicate with it, and their purchasing behavior will be enhanced.

The Impact of Social Media Marketing on Brand Awareness and Purchase Intention: Case Study of Vietnam's domestic fashion brands

The study aimed to examine the impact of social media marketing on brand awareness and purchase intention for Vietnamese domestic fashion brands. Quantitative research was conducted on 302 Vietnamese people of Generation Z. The questionnaire designed on Google forms was sent to research samples who were willing to participate. Research results determined the role and benefits of social media marketing in 2 aspects: (1) information about the brand of social media marketing on social networks and (2) brand engagement on social networks. Social media marketing has a positive impact on brand awareness and purchase intention of Vietnamese domestic fashion brands. In particular, brand information when communicating on social networks has a direct and positive impact on brand awareness and purchase intention. Brand engagement on social networks has a positive direct impact on brand awareness and a positive indirect impact on purchase intention through brand awareness. The research results show that Vietnamese domestic fashion brands do quite well in social media marketing, and are highly appreciated by the online community of generation Z in Vietnam. In the future, in order to improve brand awareness and purchase intention, Vietnamese domestic fashion brands need to pay attention to the brand information properties of social media marketing programs and need to invest more in brand engagement characteristics of social networks.
 Keywords: social media marketing, brand awareness, purchase intention

Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features.

The Influence of Social Network Characteristics in Shaping Consumers Behavior and Market Outcome

Social networks are crucial in shaping consumers behavior and influencing market outcomes. These networks connect individuals and groups, facilitating the exchange of values, ideas, and information. Consumers within these networks are influenced by their social ties and interactions, leading to changes in attitudes, perceptions, and purchasing decisions. The structure and characteristics of these networks can impact the overall market dynamics, affecting product adoption, brand awareness, and overall market performance. Thus, understanding the influence of social networks on consumers behavior and market outcomes is essential for businesses to develop effective marketing strategies and succeed in a highly connected world. This paper will utilize data from social and economic networks to analyze and identify key network attributes that are influential from various perspectives, drawing resources from an extensive real-world database and supporting theories. This study aims to provide practical implications for marketers and businesses who seek to optimize their strategies in building market success, also for researchers, academics, and other interested parties that can benefit from the results by learning how to apply network analysis in their studies.

Research on the influence of chain shareholder network on enterprise green innovation

Green innovation plays a pivotal role in the high-quality development of social economy, but its high risk and investment can daunt enterprises. As a result, business stakeholders often use interpersonal relationships to start to overcome boundaries to obtain and disseminate green innovation information and resources. Based on the number of green innovation patent applications from 2011 to 2020, this paper constructs an undirected weighted network of enterprise chain shareholders and empirically examines the influence of centrality and structural hole characteristics of social networks on green innovation. Additionally, a heterogeneity analysis is conducted from the perspectives of core-periphery structure and CEO overseas experience. The findings are beneficial for enterprises to have achieve a more comprehensive understanding of the role of their positional advantages in the network on green innovation and provide new ideas and directions for firms to improve their green innovation level.

Associations Between Social Network Characteristics and Brain Structure Among Older Adults.

Social connectedness is associated with slower cognitive decline among older adults. Recent research suggests that distinct aspects of social networks may have differential effects on cognitive resilience, but few studies analyze brain structure. This study includes 117 cognitively impaired and 59 unimpaired older adults. The effects of social network characteristics (bridging/bonding) on brain regions of interests were analyzed using linear regressions and voxel-wise multiple linear regressions of gray matter density. Increased social bridging was associated with greater bilateral amygdala volume and insular thickness, and left frontal lobe thickness, putamen, and thalamic volumes. Increased social bonding was associated with greater bilateral medial orbitofrontal and caudal anterior cingulate thickness, as well as right frontal lobe thickness, putamen, and amygdala volumes. The associations between social connectedness and brain structure vary depending on the types of social enrichment accessible through social networks, suggesting that psychosocial interventions could mitigate neurodegeneration. Distinct forms of social capital are uniquely linked to gray matter density (GMD). Bridging is associated with preserved GMD in limbic system structures. Bonding is associated with preserved GMD in frontal lobe regions. Bridging is associated with increased brain reserve in sensory processing regions. Bonding is associated with increased brain reserve in regions of stress modulation.

Linguistic Characteristics and Stylisation of Korean Cinema Based on the Social Network Analysis Method

Abstract Korean movies have consistently produced exceptional works that have triggered discussions and spread on social networks in recent years. In this paper, we take the movie language dissemination characteristics in social networks as an anchor point and combine the scale and structure of social networks to improve on the IC model to obtain a movie information dissemination model. This network model and the information cascade tandem are selected as the core of density peak clustering, and improvements are made for the updating rules as well as the propagation order of the multi-strategy labeling algorithm in the DCN algorithm. The characterization of movie language propagation in social networks is completed by using an improved algorithm with a multi-labeling idea. The overall stylistic evolution of Korean movies is examined, and then different language feature styles are compared according to social propagation. In the movie Memories of Murder, at 32 minutes and 23 seconds, which shows a rainy night on a muddy land and conveys the hardship of the protagonist’s situation through visual language, the propagation heat reaches 10.52. Similar to this sentence, the movie depicts a rainy night at 63 minutes and a crime scene at 108 minutes, with the propagation heat of the visual language reaching 19.48 and 23.48, respectively. Based on the propagation analysis of the social networks, the accurate capture of the unique impression of the movie language among the audience.

The politics of vaccination: a closer look at the beliefs, social norms, and prevention behaviors related to COVID-19 vaccine uptake within two US political parties

ABSTRACT COVID-19 vaccine hesitancy and suboptimal vaccine uptake rates are pressing public health challenges. Vaccine hesitancy has been observed for different vaccines. For COVID-19 vaccines, multiple factors influence vaccine uptake in the U.S. including political ideology. A more nuanced understanding of the factors influencing COVID-19 vaccine uptake within political parties is needed. This study assesses the relationship between known vaccine hesitancy factors and vaccine uptake within two major political parties. Data from 804 U.S. participants in an online survey from June 2021 was used to assess the association between COVID-19 vaccine uptake (no dose vs. any dose) and categories of factors thought to influence vaccine uptake (sociodemographic variables, COVID-19 disease and vaccine belief variables, belief in COVID-19 prevention behavior variables, and social network features variables) for Republicans and Democrats using bivariate and multivariate regression. 65.4% of the sample reported having received at least one dose of the COVID-19 vaccine (22.6% Republican and 52.1% Democrat). In the total sample bivariate model, Democrat participants had significantly greater odds of having received a dose of the vaccine as compared to Republican participants (OR = 2.51, 95%CI = 1.73–3.64). In adjusted models, the speed of vaccine development was negatively associated with vaccine uptake for both Republicans (aOR = 0.18, 95%CI = 0.06–0.57) and Democrats (aOR = 0.40, 95%CI = 0.19–0.86), as was concern about side effects from the vaccine (Republicans: aOR = 0.15; 95%CI = 0.05–0.47; Democrats: aOR = 0.14, 95%CI = 0.06–0.31). COVID-19 vaccine uptake among Republicans, but not Democrats, was associated with belief that the vaccine prevents COVID-19 (aOR = 3.29, 95%CI = 1.29–8.37) and belief about friends’ vaccine intentions (aOR = 6.19, 95%CI = 2.39–16.05). There were no significant factors unique to Democrats. Concerns about aspects of COVID-19 vaccine safety for both political groups suggest that vaccine advocacy interventions should universally address these factors. However, Republican beliefs in vaccine efficacy and in friends’ vaccine intentions suggest a need for Republican-specific messaging.

Social Networks, the COVID-19 Pandemic, and Emerging Adults' Mental Health: Resiliency Through Social Bonding and Cohesion.

Objectives. To assess how personal social network characteristics moderated mental health declines during the COVID-19 pandemic in emerging adults compared with other age groups. Methods. The Person to Person Health Interview Study, a representative, probability-based cohort study (n = 2485) in Indiana, collected data through face-to-face (baseline) and phone (follow-up) interviews before and during the pandemic. We used survey-weighted growth curve models to examine network effects on computer-adaptive testing measures of depression and anxiety severity. Results. Respondents reported significantly increased depression and anxiety in 2021, which returned almost to baseline levels for most age groups by 2022 (P < .001). Stronger ties to others and more interconnected ties were significantly associated with lower depression (B = -0.112 [P < .05]; B = -0.086 [P < .001]) and anxiety (B = -0.101 [P < .05]; B = -0.063 [P < .01]) severity across the pandemic. Interaction models revealed disproportionate protective effects of network characteristics on depression (B = -0.456 [P < .001]; B = -0.268 [P < .001]) and anxiety (B = -0.388 [P < .001]; B = -0.284 [P < .001]) for emerging adults. Conclusions. Cohesive and affectively strong personal networks promote resiliency to common mental health challenges during periods of crisis, particularly for emerging adults whose social roles and relationships were disrupted during a critical period of development. (Am J Public Health. 2024;114(S3):S258-S267. https://doi.org/10.2105/AJPH.2023.307426).

Learning Analytics in Education: A Social Network-Based Approach for Analyzing the Interaction and Influence of Collaborative Learning Communities

Collaborative learning is viewed as an increasingly important learning mode in higher vocational education these days. In this mode, students are no longer passive receivers of knowledge, but take the roles of creators and sharers, and figuring out the interactive and collaborative relationships between students is particularly important for understanding the pattern and structure of student interaction. With the help of social network analysis methods, this study investigated the social network features of collaborative learning communities, measured the parameters of these network features, analyzed the accessibility of community members, and revealed the influence of members in the community based on the Lead index. The findings of this paper give deeper understandings and new insights into the collaborative learning mode and provide useful evidences for the optimization of collaborative learning strategies.

Daily Peer Relationships and Academic Achievement among College Students: A Social Network Analysis Based on Behavioral Big Data

This study aimed to detect college students’ daily peer networks through a behavioral big-data-driven social network analysis and to explore the relationship between college students’ daily peer relationships and academic achievement. We collected data on the class attendance, eating, and bathing records of 4738 undergraduate students who entered a university in 2018 to infer the daily peer relationship networks of students. The Louvain algorithm and some network indicators such as density and average clustering coefficient were used to investigate social network characteristics of peer relationship networks. The findings show that initially, students in the same dormitory tended to form daily peer relationships, gradually shifting toward relationships centered on classmates as time progressed. These peer networks often aligned with geographical location and living areas. Moreover, the peers of college students who received high-level scholarships were more likely to receive scholarships. The number of peers was positively correlated with the likelihood of receiving a scholarship. The research findings contribute to the application of information technology to promote the sustainable development of higher education and individual students.

Innovation and Development of Cultural Construction Based on the Characteristics of Social Cognitive Networks in the Sinicization of Marxist Theory

Abstract By exploring the characteristics of Marxism’s Chineseization, an innovative path for cultural construction development is proposed. Based on the composition of cultural carriers of Marxist Chineseization, this paper constructs the social cognitive network of Marxist Chineseization. The structural characteristics of the network are studied based on node influence, proximity centrality, etc., thus proposing a scale-free model in the social network and adopting Pearson coefficients to characterize the relevance of the nodes. Accordingly, the process of cultural identity and the evolution of cultural carriers in the Chineseization of Marxism are analyzed, and the characteristics of cultural viewpoints are analyzed. In the construction of institutional culture, the cultural identity increased from 0.44 in 1980 to 0.832 in 2019. In constructing spiritual culture, the cultural identity increased from 0.389 in 1980 to 0.822 in 2019. With the dissemination and localization of Marxist theories in China, cultural identity is a process of zigzagging.

Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance

BackgroundDimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data. Particularly in cancer research, when reducing the number of features, i.e., genes, it is important to select the most informative features/potential biomarkers that could affect the diagnostic accuracy. Therefore, researchers continuously try to explore more efficient ways to reduce the large number of features/genes to a small but informative subset before the classification task. Hybrid methods have been extensively investigated for this purpose, and research to find the optimal approach is ongoing. Social network analysis is used as a part of a hybrid method, although there are several issues that have arisen when using social network tools, such as using a single environment for computing, constructing an adjacency matrix or computing network measures. Therefore, in our study, we apply a hybrid feature selection method consisting of several machine learning algorithms in addition to social network analysis with our proposed network metric, called the corrected degree of domesticity, in a single environment, R, to improve the support vector machine classifier’s performance. In addition, we evaluate and compare the performances of several combinations used in the different steps of the method with a simulation experiment.ResultsThe proposed method improves the classifier’s performance compared to using the whole feature set in all the cases we investigate. Additionally, in terms of the area under the receiver operating characteristic (ROC) curve, our approach improves classification performance compared to several approaches in the literature.ConclusionWhen using the corrected degree of domesticity as a network degree centrality measure, it is important to use our correction to compare nodes/features with no connection outside of their community since it provides a more accurate ranking among the features. Due to the nature of the hybrid method, which includes social network analysis, it is necessary to investigate possible combinations to provide an optimal solution for the microarray data used in the research.

Social Network Characteristics and Depressive Symptoms of Italian Public Health Medical Residents: The Public Health Residents' Anonymous Survey in Italy (PHRASI).

Despite the high impact of the COVID-19 pandemic on social interactions and healthcare workers' (HWs') mental health, few studies have investigated the association between social network characteristics and HWs' mental health, particularly during the pandemic. Therefore, we aimed to assess the associations between public health residents' (PHRs') social network characteristics and depression. We used data from the Public Health Residents' Anonymous Survey in Italy (PHRASI), a nationwide cross-sectional study. Social network characteristics were self-reported. Depressive symptoms were assessed using the nine-item Patient Health Questionnaire. Linear and logistic models adjusted for age, sex, physical activity, and alcohol were used. A moderation analysis by sex was also performed. A total of 379 PHRs participated in the survey (58% females, median age of 30 years). More peer-to-peer (odds ratio OR = 0.62 (0.47-0.83)) and supervisor support (OR = 0.49 (0.36-0.68)), more social participation ((OR) = 0.36 95% CI (0.25-0.50)), and having a partner (OR = 0.49 (0.25-0.96)) were significantly associated with a lower risk of clinically relevant depressive symptoms. Work-to-private-life interference was significantly associated with a higher risk of clinically relevant depressive symptoms (OR = 1.77 (1.28-2.45)). Promoting a supportive work environment and social participation as well as reducing work-to-private life interference can contribute to reducing the high burden among PHRs.

Evaluation of Family and Community Social Network Characteristics Among High-Risk Family Members to Improve Cancer-Related Health Behaviors.

To evaluate the effects of social network characteristics of individuals with a family history of cancer on the use of cancer-related services (e.g., screening, genetic counseling/testing). 170 family members of individuals with the most common hereditary or familial cancers. Data collection occurred between March and September 2021 using an online survey. Having strong within-immediate family relationships and family members who underwent more screening procedures was associated with increased breast cancer screening, and having more family members with cancer was associated with colorectal cancer screening. Having a large family, having family members who underwent screening for more cancers, and having strong social cohesion among families were associated with an increased rate of genetic testing. Nurses working with families affected by cancer should focus on strategies to strengthen relationships among family members to improve knowledge of cancer screening and available genetic services.

Dynamic community detection based on graph convolutional networks and contrastive learning

With the continuous development of technology and networks, real-life interactions are gradually being abstracted into social networks for study. Social circles are a fundamental structural feature that is prevalent on social networks. Thus, exploring social circle structure plays an important role in revealing the characteristics of complex social networks and provides guidance for understanding social behavior in real life. For example, it can aid in precision marketing, personalized recommendation, and knowledge dissemination within social circles. One of the important means of identifying social network circles lies on community detection algorithms. However, real-world social networks are often dynamic and can be studied and analyzed by building dynamic networks, while existing dynamic network community detection methods tend to ignore the global structure information and time-series information of nodes. To address this problem, this paper proposes a dynamic network community detection algorithm based on graph convolutional neural networks and contrastive learning, which fully captures the adjacent characteristics between nodes based on the correlation information and leverages the feature smoothing strategy to efficiently extract node representations of dynamic networks under unsupervised scenario. Specifically, the proposed algorithm first utilizes node correlation based aggregation strategy to compute the feature matrix for single time-step of the dynamic network. Then, mutual information maximization is implemented based on cross-entropy between learned local and global representations. To reduce the computational overhead in the optimization process, an additional LSTM module is further equipped for updating the parameters of graph convolutional networks in each time-step. Additionally, a contrastive learning based network smoothing strategy is designed to minimize the feature differences between neighboring nodes. Comparative experiments demonstrate that the proposed algorithm achieves excellent performance on both synthetic and real networks.

Social-aware energy management in D2D communications

The rapid evolution of cellular networks (4G and 5G), has led to an unprecedented increase in network traffic, driven by both the growing number of mobile users and the escalating data consumption per user. This surge significantly impacts energy consumption and quality of service (QoS). Device to Device (D2D) communication technology has emerged as a promising solution. However, D2D communication encounters substantial challenges, particularly in the domain of energy management. This paper presents a novel social-aware framework designed to address the energy management challenges in D2D communications. It explores how users' social network characteristics can optimize D2D communication for enhanced energy efficiency. The framework includes two innovative methods, SOCICHS and SOCICF, specifically developed for cluster head selection and cluster formation. These methods seamlessly integrate both social and physical information to facilitate energy-efficient D2D communication. Additionally, we propose a comprehensive model for D2D communication management within this framework. To evaluate the effectiveness of the approach, extensive experiments were conducted, involving a maximum population of 1200 users and the consideration of various coefficient values (τ1, τ2). The use of the GOWALLA dataset revealed an average energy efficiency improvement of 25 % and 31 % when compared to base scenarios. Likewise, the analysis of the BRIGHTKITE dataset showed energy efficiency enhancements of 23 % and 32 %. These findings reveal the significant impact of social-aware clustering on energy management within D2D communications. Moreover, the integration of physical features, such as distance, into the framework demonstrated its additional value in achieving efficient energy consumption.