Properties Of Social Networks Research Articles

Visualizing the Influence of Social Networks on Recovery: A Mixed-Methods Social Identity Mapping Study with Recovering Adolescents

Background: Social recovery capital (SRC) refers to resources and supports gained through relationships and is vital to adolescent addiction recovery. Much is known about how substance use relates to social networks, but little is known about how other dimensions of social networks influence recovery (e.g., network size/exposure, degree of conflict). Methods: This mixed-methods study sampled 28 adolescents who received treatment for alcohol and other drug (AOD) use disorder (14–19 yrs.: 71% male; M = 17.32 yrs., SD = 1.33; White 82%): 20 were recovery high school (RHS) students. Adolescents completed a social identity map for addiction recovery (SIM-AR), survey, and interview. Qualitative data were content analyzed and the data from the SIM-AR were quantified. Results: On average, participants reported belonging to five distinct groups within their network (Range, 2–9; SD = 1.63; M = 27.89 people, SD = 20.09). Of their social network connections, 51% drank alcohol and 46% used other substances, on average. Larger networks involved more conflict (r = 0.57). Participants were more likely to spend more time with groups that had greater proportions of non-substance-using members. These linkages were stronger for RHS than for non-RHS students. Qualitative analyses revealed that youth reported their recovery-oriented groups as supportive, but some reported that their substance-using friends also supported their recovery. Discussion: SIM-AR was a useful measurement tool, and, through qualitative interviews, we identified unique aspects of youths’ social networks important for further examination. Research with recovering youth should examine SRC-related elements within their networks including relationship quality, belonging, and conflict, alongside the substance use behaviors of network members.

The gene “degrees of kevin bacon” (dokb) regulates a social network behaviour in Drosophila melanogaster

Social networks are a mathematical representation of interactions among individuals which are prevalent across various animal species. Studies of human populations have shown the breadth of what can spread throughout a social network: obesity, smoking cessation, happiness, drug use and divorce. ‘Betweenness centrality’ is a key property of social networks that indicates an individual’s importance in facilitating communication and cohesion within the network. Heritability of betweenness centrality has been suggested in several species, however the genetic regulation of this property remains enigmatic. Here, we demonstrate that the gene CG14109, referred to as degrees of kevin bacon (dokb), influences betweenness centrality in Drosophila melanogaster. We identify strain-specific alleles of dokb with distinct amino acid sequences and when the dokb allele is exchanged between strains, flies exhibit the betweenness centrality pattern dictated by the donor allele. By inserting a GAL4 reporter into the dokb locus, we confirm that dokb is expressed in the central nervous system. These findings define a novel genetic entry point to study social network structure and thereby establish gene-to-social structure relationships. While dokb sequence homology is exclusive to Diptera, we anticipate that dokb-associated molecular pathways could unveil convergent neural mechanisms of social behaviour that apply in diverse animal species.

Analyzing the Possibilities of Implementation of AI and Social Networks in Teaching Foreign Language Students: Ukrainian Universities Case Study

The growing integration of artificial intelligence (AI) and social networks into the educational landscape has raised questions about their impact on teaching and learning, particularly in foreign language instruction. This study explores the didactic properties of social networks and AI as information and communication technology (ICT) tools in the context of foreign language education at the university level. Through a comprehensive literature review, the researchers analyzed global studies on the integration of AI and social networks in language learning, as well as research specific to the Ukrainian higher education context. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was conducted to systematically evaluate the potential benefits and challenges associated with the implementation of these technologies in foreign language instruction. The findings indicate that using AI-driven language learning applications, and social media platforms can significantly enhance vocabulary retention, language proficiency, and student engagement. However, the study also highlights potential threats, such as risks to academic integrity, entrenchment of discrimination, and the democratization of plagiarism. The research further emphasizes the importance of addressing teachers’ skills in navigating the ethical implications of AI integration in educational settings. The study provides valuable insights for university administrators, language instructors, and educational technology researchers. It underscores the need for a nuanced and informed approach to incorporating AI and social networks into foreign language curricula, balancing the pedagogical benefits with appropriate safeguards to ensure the quality and integrity of the educational process. The findings contribute to the evolving discourse on the role of emerging technologies in transforming higher education and language teaching.

Exploring social networks through stochastic multilayer graph modeling

Several graph models are available today to model online social networks. These graph models are used to analyze the structural properties of the online social network, such as detecting communities, finding the influential spreader and predicting the behavior of the network. However, these models are based on deterministic single-layer graphs that may not be appropriate when online users use multiple social networks at the same time and social networks provide specific services. Moreover, because of the unknown and dynamic nature related to the behaviors and activities of online users, as well as structural and behavioral parameters, which may vary over time, stochastic multi-layer models could be applied to better capture and represent this phenomenon, as well as the dynamic nature of social networks. For example, in recommender systems, users' interests are unknown parameters and vary over time. Therefore, stochastic multilayer graph modeling can be used to develop recommender systems by considering different layers for different types of interests or preferences. In this paper, we propose a stochastic multilayer graph in which the edges are associated with random variables as a potential graph model for the analysis of online social networks. Therefore, after redefine some network measures related to stochastic multilayer graphs, we propose a Cellular Goore Game (CGG) based algorithm to computes the redefine network measures. A CGG-based algorithm computes defined network measures by learning automata from the edges of stochastic multilayer graphs. The experimental results show that the new CGG-based algorithm requires fewer samples from the edges of stochastic multilayer graphs than the standard sampling method in network measures calculation. Furthermore, the obtained results demonstrate that, from an evaluation perspective, the CGG-based algorithm provides superior results in terms of Kolmogorov-Smirnov (KS-test), Pearson Correlation Coefficient (PCC), Normalized Root Mean Square Error (NRMSE) and Kullback–Leibler divergence (KL-test).

Study on the impact of social capital on the rural residents’ conscious interpersonal waste separation behavior: evidence from Jiangxi province, China

Guiding rural residents to implement interpersonal waste separation in their daily lives consciously is crucial for controlling solid waste pollution in developing countries. This paper utilizes survey data from Jiangxi Province which is one of the national pilot zones for ecological conservation in China to analyze the impact of the social capital that includes social networks, social trust, and social norms on the rural residents’ conscious interpersonal waste separation behavior. The empirical results indicate that social capital has a positive effect on the rural residents’ conscious interpersonal waste separation behavior, wherein the effects of social networks and social trust are significant. Among the three dimensions of social capital, social networks and social norms are substitutable, while social trust and social norms have a complementary effect on each other. Moreover, the ecological cognition and subjective norm play a significant mediating role in the relationship between social network, social trust, and social norms and the rural residents’ conscious interpersonal waste separation behavior, while the government policies plays a significant moderating effect.

Navigating the social maze: An integrative review on the social network properties of deviant peer influence in adolescent networks

Navigating the social maze: An integrative review on the social network properties of deviant peer influence in adolescent networks

Designing profitable seeding Programs: The effects of social network properties and consumer homophily

This study explores the main and interaction effects of consumer social network properties and homophily on the profit impact of seeding programs. Social network properties capture the topology of the connections between consumers, while homophily captures the degree to which consumers share similar attributes with their social connections.We conduct extensive agent-based simulation experiments on eight empirical social networks mapped from Facebook, Twitter, and Emails, among others. The results reveal that seeding programs generate higher profits in clustered social networks, and are less profitable when the network diameter and average distance between consumers increase. The effects of social network diameter and average distance are only observed when homophily is high and are negligible when homophily is low. For random seeding and targeting social hubs, the effect of social network clustering on profits is stronger when homophily is high. For early adopters and experts, the effect depends on the attribute on which homophily exists in the market.We explain the findings by distinguishing between the effects of seeding programs on local diffusions—effects on direct acquaintances—and global diffusion processes—the impact across the social network. The findings offer critical implications for marketers aiming to maximize the return-on-investment in seeding programs.

How Networks Affect and Contagion Individuals Social Behavior of Drinking

The research analyzes how networks affect an individuals social behavior of drinking by researching perspectives of both micro and macro, especially in the aspects of how different personalities and connections between individuals and their social network, and the development and structure of their social network. This document discusses and shares how a variety of information and behaviors are transmitted through social networks, and describes different contagion methods and social network structures, leading to different spread and diffusion efficiency results. The study adopts questionnaire surveys and longitudinal measures to explore different people's personalities and social networks through different question settings to discover the impact and relationships among them. Through analyzing a large number of samples and analyzing the different impacts brought by nodes with different personalities, three effective influences on personality and certain social network properties can be derived for having impacts on social behaviors.

38 Application of Real-Time Location Systems for Automated Behavioral Monitoring in Sheep

Abstract Evaluating how, when, and where an animal moves in their environment can provide insights into their health and welfare states. Spatial tracking systems for monitoring sheep are less well developed than accelerometer-based systems, but have the potential to add important behavioral information to Precision Livestock Farming (PLF) applications. Global Positioning Systems (GPS) can be used to track pasture-based animals, but are not suitable to establish indoor locations. Real-Time Location Systems (RTLS) are a recent development that use radio-frequency signals emitted by tags to track the indoor location of an animal online and continuously. Raw RTLS data in the form of x, y coordinates can be used to calculate multiple spatial metrics, including velocity, acceleration, distance moved, the proportion of time an animal spends in different functional areas, social network attributes, and range size. RTLS can also be integrated with accelerometers to potentially improve predictive accuracy of behavioral algorithms. RTLS have been primarily tested and validated for indoor tracking of dairy cattle and have been used to assess behavioral time budgets and to detect space-use changes related to estrus or health status. For on-farm applications, these data must be transformed into threshold-based algorithms to generate notifications on changes in the health or welfare status of an animal. Algorithm development needs to account for confounding factors such as breed, sex, age, parity, and stage of lactation, as well as environmental variability that can influence the performance of decision-making tools under real-life conditions. In addition, independent validation studies of commercial systems are lacking, and many calculate positioning error with stationary objects rather than moving animals, potentially inflating accuracy estimates. In conclusion, there is growing empirical support for the use of RTLS as a PLF technology for on-farm animal health and welfare assessment. However, research is needed to validate these systems in sheep and identify spatial metrics that are accurate and robust predictors of health and welfare states for commercial applications.

Sustainability of the local stakeholder network in semi-steppe rangelands in southern Iran

Sustainable land management (SLM) as a holistic method seeks to combine ecological values with socio-cultural and policy concepts, through which local populations play a significant role in achieving this management. Qasr Yaqoub village was selected for the present study, which is located in Safashahr city in Fars province, south of Iran. The common method of herding in Qasr Yaqoub village is participatory and in the form of assistance systems. Therefore, in order to have a comprehensive evaluation of the stability of the local stakeholder network in different dimensions of sustainability and social networks, two methods of indicators analysis including social network analysis (SNA) and sustainability barometer model (SBM) were used. The index values for trust and social cooperation ties among stakeholders in the local stakeholders' network in Qasr Yaqoub village, consisting of 42 individuals, were found to be 63.1% and 59.8%, respectively. These values indicate a stable density in the trust network and an average density in the cooperation network among stakeholders in the region. In addition, the social network index, which measures the extent of connections, is 1086 for trust and 1030 for social cooperation, both covering more than 50% of the expected ties. This signifies a significant level of interaction between stakeholders regarding relevant issues, facilitating the promotion of social cohesion in the study area. These findings highlight the positive dynamics within the stakeholder network, fostering trust, cooperation, and social cohesion in Qasr Yaqoub village. This study showed that the combination of network analysis and sustainability barometer model could be a useful tool in the sustainability analysis of local communities in rangeland ecosystems.

Navigating the Social Maze: An Integrative Review on the Social Network Properties of Deviant Peer Influence in Adolescent Networks

ABSTRACT Social network analysis (SNA) holds great potential for examining the influence of peers and group dynamics on the development of criminal behavior among adolescents as it offers a robust framework for studying complex social interactions. However, there needs to be more knowledge regarding key research findings on deviant adolescent influence using a social network approach. In this integrative literature review, we examine what type of information is used to construct adolescents’ social networks, how network compositions affect deviant influence, and how deviant influence is transmitted. For this review, we selected 39 articles by searching Scopus and Web of Science. The results demonstrate that SNA offers valuable insights into the dynamics of deviant peer influence among adolescents. However, there are also conflicting findings that need further exploration. Future research could focus on these discrepancies and continue to leverage the power of SNA in studying the development of criminal behavior in adolescents. This review highlights the importance of SNA in better understanding the mechanisms behind peer influence and provides a roadmap for future research in this field.

Association between social networks and symptoms of post-traumatic stress during the pandemic: Cohort study in South Korea

BackgroundThis study estimated the association between pre-pandemic social network properties and symptoms of posttraumatic stress during the coronavirus disease 2019 (COVID-19) pandemic in South Korea. MethodsWe conducted four online mental health surveys during COVID-19 (from March 14, 2020, to December 11, 2021) among individuals enrolled in a community-based cohort study (previously recruited from 2013 to 2018). Among 4060 people interviewed at the pre-pandemic baseline, 2652 individuals (men = 951, women = 1701) who responded to at least one of the four surveys conducted were included. At baseline, each individual's social network, including size and average closeness, was measured in an egocentric way. Post Traumatic Stress Disorder Checklist for DSM-5 (PCL-5) was measured at each survey point. A generalized linear model and a generalized linear mixed model were used after stratified by sex. ResultsAmong men, network size was negatively associated with total PCL-5 score (b = −0.42, SE = 0.16, p = 0.002). The magnitude of the association increased over time and was maximal by the 4th survey (b = −0.65, SE = 0.31, p = 0.037). Among women, average closeness was negatively associated with the total PCL-5 score (b = −1.16, SE = 0.37, p = 0.002). In analyses disaggregated by symptom clusters, social networks were associated explicitly with symptoms of intrusion and mood but not with avoidance and arousal symptoms. ConclusionIn this cohort of adults followed for more than a year during the outbreak of COVID-19 in South Korea, social networks established before the onset of the COVID-19 pandemic had a protective association against developing symptoms of PTSD during the first two years of the pandemic.

Research on the influence of social network on young people’s capacity of innovation and entrepreneurship based on stochastic forest model

Abstract In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the Bagging algorithm is to use the CART regression tree algorithm to make the variance as small as possible while ensuring that the average performance of the model does not drop significantly. Next, the random forest model was improved by using the feature importance measure algorithm and the F-measure weighting algorithm, which improved the overall performance of the random forest model. Then the elements of innovation and entrepreneurship ability of college students were identified, and the basic characteristics of the research subjects were obtained using the scale, and the influence of social networks on innovation and entrepreneurship ability based on the random forest model was studied empirically. The results show that the coefficients of the two dimensions of association strength and heterogeneity in the dimensions of social networks of college students are larger, 0.251 and 0.222, respectively, indicating that it is the network association strength and heterogeneity that have a greater impact on the improvement of innovative thinking ability. This study promotes innovation and entrepreneurship education in colleges and universities, which is important for the improvement of students’ innovation and entrepreneurship ability.

ВЗАЄМОЗВ’ЯЗОК САМООЦІНКИ ТА САМОПРЕЗЕНТАЦІЇ МОЛОДІ У СОЦІАЛЬНИХ МЕРЕЖАХ

The article describes the features of correlation of self-esteem and self-attitude of youth that uses social networks. The phenomenon of self-presentation has acquired great significance and relevance due to the emergence and spread of social networks that involve a demonstration of one’s Self in the dimension of social networks and taking of a certain position. In modern world, social networks help young people feel connected to others and present themselves as individuals. Self-esteem is an assessment that a person gives to themselves, their qualities and abilities, as well as their place in the social environment. A person's self-esteem shows their social adaptation, it plays a role of a regulator of behaviour and activity that accumulates the entire life experience of an individual. The phenomenon of self-presentation may be interpreted within one of three paradigms: 1) self-presentation is defined as a general characteristic of an individual's social behaviour, that is, purposeful and conscious behaviour aimed at creating a certain impression on others; the process or the subject’s constant desire to present the desired image to the external and internal public); 2) self-presentation as a mean for elimination of cognitive dissonance; 3) there is a relation between person’s self-presentation and self-awareness. The results of an empirical study of the features of the correlation between self-esteem and self-presentation of young people in social networks are presented. It was found empirically that half of the interviewees had a low level of self-esteem, it was also found that the self-presentation tactics of the assertive type predominate in terms of manifestation, and the tactics of the defensive type acquired comparatively less manifestation. In accordance with the results of the correlation analysis, it was determined that when the level of self-esteem increases, young people use defensive type defensive type of self-presentation less. In other words, when the level of self-esteem increases, young people begin to lean towards self-presentation strategies aimed primarily at managing impressions, and not at protecting their own image in the eyes of others.

Exploring Socio-Political Dimensions of Heritage Language Maintenance: The Case of Vietnamese Speakers in Montréal

Research on heritage language (HL) development often focuses on immigrants’ identity and social network as predictors of HL maintenance. However, an important and overlooked factor is the socio-political circumstances that trigger emigration, such as whether immigrants relocate due to economic hardship or political turmoil. This study examines if the pattern of HL maintenance and its association with immigrants’ ethnolinguistic identity and social engagement differ for families of political versus economic immigrants. Participants were 38 parent–child pairs from Montreal’s Vietnamese diaspora. The parents identified their reason for immigration and completed an ethnolinguistic questionnaire, their children provided a HL communication profile, and all participated in interviews. To determine participants’ Vietnamese speaking skills, short interview excerpts were rated for accentedness, comprehensibility, fluency, and global knowledge. Participants were generally successful at maintaining their HL, but there was a decline in the children’s Vietnamese, especially for the economic immigrants. Several ethnolinguistic variables (desire to preserve a HL, pride in heritage culture) and social network properties (network size, intimacy, communication-related stress) appeared to contribute to HL maintenance. However, these relationships were different (and sometimes opposite) in the groups of political versus economic immigrants, suggesting that HL development and maintenance are subject to various contextual influences.

C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

Effects of land recuperation on farmers’ social capital: a Chinese field analysis

ABSTRACTSocial capital is an integral part of farmers’ life, which can be exogenously affected by land recuperation. Based on 1240 farmer field survey data in Gansu Province, this paper used the Logit model to analyse the influencing factors of farmers’ participation in land recuperation, and used the entropy method to measure social capital from the three dimensions of social network, social trust and social norms, and further used the propensity matching score method to measure the effect of land recuperation on farmers, and then compared the effects under different fixed ages and education groups. The following factors significantly affected farmers’ participation in land recuperation: age, years of education, migrant workers’ relationships with family and friends, relationship between migrant workers and friends and colleagues in the workplace, number of migrant workers away from home, cultivated land area, and family living standards. Land recuperation had the greatest promotion effect on farmer’ social network (163.9%), followed by social trust (28.0%) and social norm (11.3%). According to the results of group differences, land recuperation most significantly affected the social capital of farmers aged 45–55 years and household heads educated for 9–12 years compared to other age and education groups.

Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.

Multidimensional poverty vis-à-vis climate change vulnerability: Empirical evidence from flood-prone rural communities of Charsadda and Nowshera districts in Pakistan

Pakistan is among the top five countries that have been severely impacted by extreme weather events. Its population, a majority of which is poor, is highly vulnerable to the adverse effects of climate change. This study aims to examine the correlation between multidimensional poverty and climate change vulnerability in the rural areas of the Nowshera and Charsadda districts of the Khyber Pakhtunkhwa province. Data were collected from 452 households through a pre-tested questionnaire that assessed poverty and climate change vulnerability. The multidimensional poverty index, based on the United Nations Development Programme and Oxford Poverty and Human Development Initiative, was used to determine poverty levels, while the livelihood and climate change vulnerability indices were developed to quantify the level of climate change vulnerability. The results of the study revealed that half of the population in the study area falls below the multidimensional poverty threshold. Nowshera had a higher number of multidimensionally poor people, and the intensity of poverty was greater than in Charsadda. Nowshera was found to be more deprived in all three dimensions of poverty, i.e., education, health, and standard of living. However, in terms of climate change vulnerability, Charsadda lagged behind in the dimensions of livelihood strategies, social networks, socio-demographic profiles, and food. On the other hand, Nowshera was found to be more vulnerable in the dimensions of natural assets, financial assets, and health. Overall, the results showed that Nowshera was more vulnerable to floods compared to Charsadda. The study found a strong positive correlation between the multidimensional poverty index and the climate change vulnerability indices, implying a close relationship between the two concepts.

Analysis of Influencing Factors of Farmers’ Homestead Revitalization Intention from the Perspective of Social Capital

Revitalizing idle rural residential bases is essential for improving the utilization rate of residential resources, activating pastoral land resources assets, increasing farmers’ property income, and stimulating rural development vitality. Social capital is one of the essential social resources owned by farmers, which is closely related to rural social governance and farmers’ daily lives and plays an indispensable role in revitalizing residential land. Based on the theory of social capital and the survey data of 316 farm households in Shaanxi Province in 2022, this study first empirically analyzes the influencing factors of social capital and its constituent dimensions on farm households’ willingness to revitalize their home-steads using a Logit regression model, and then empirically analyzes the hierarchical structure relationship among the influencing factors using an ISM model. The results of the study show that: (1) Social capital and its three constituent dimensions of social network (“Contact with relatives, neighbors, and families” and “Contact with village committee staff”), social trust (“Trust in friends and relatives” and “Trust in village committee and other organizations”), and social participation (“Participation in meetings of village committees and other organizations”) have significant positive effects on farmers’ willingness to revitalize their residential land. (2) There are differences in the effects of different dimensions of social capital on farmers’ willingness to revitalize their homesteads: social trust as a deep-seated cause affects farmers’ social networks and social participation, which in turn affects farmers’ knowledge of homestead revitalization policies and ultimately farmers’ willingness to revitalize their homesteads. Based on the findings of the study, to improve farmers’ willingness to revitalize their homestead bases, government departments should base their efforts on the social capital characteristics of farmers, increase the cultivation of farmers’ social capital, and strengthen the publicity of the work of revitalizing homestead bases and related policies.