Strength Of Social Relationships Research Articles

Normativity vs. uniqueness: effects of social relationship strength on neural representations of others.

Understanding others involves inferring traits and intentions, a process complicated by our reliance on stereotypes and generalized information when we lack personal information. Yet, as relationships are formed, we shift toward nuanced and individualized perceptions of others. This study addresses how relationship strength influences the creation of unique or normative representations of others in key regions known to be involved in social cognition. Employing a round-robin interpersonal perception paradigm (N = 111, 20 groups of five to six people), we used functional magnetic resonance imaging to examine whether the strength of social relationships modulated the degree to which multivoxel patterns of activity that represented a specific other were similar to a normative average of all others in the study. Behaviorally, stronger social relationships were associated with more normative trait endorsements. Neural findings reveal that closer relationships lead to more unique representations in the medial prefrontal cortex and anterior insula, areas associated with mentalizing and person perception. Conversely, more generalized representations emerge in posterior regions like the posterior cingulate cortex, indicating a complex interplay between individuated and generalized processing of social information in the brain. These findings suggest that cortical regions typically associated with social cognition may compute different kinds of information when representing the distinctiveness of others.

Assessing Social Relationship and Network Strength among Sports Organizations and Athletes: A Study on Rajshahi City Corporation

In countries like Bangladesh, people often do not consider sports as a viable career option, which is why they tend to avoid it. However, nowadays people are becoming more advanced in both body and mind. In this scenario, the level of social interaction should increase between sports organizations and athletes. The aim of this study is to examine the level of relationship between sports organizations and athletes through empirical research that uses both qualitative and quantitative data. The researchers have collected primary data through face-to-face interviews and focus group discussions from a sample of 102 individuals, including 91 athletes and 11 club authorities. The main outcome of this research is that many social development initiatives were undertaken by sports organizations, which brought all the athletes together and formed an incredible social relationship among them.

Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

Adaptive Capacity Task Offloading in Multi-Hop D2D-Based Social Industrial IoT

Traditional communication technologies such as cellular networks are facing problems to support high service quality when used for time-critical applications in an Industrial Internet-of-Things (IIoT) context, including real-time data transmission, route dependability, and scalability. To address these problems, device-to-device (D2D) communications based on social relationships can be used, which allow for task-offloading: resource-rich devices share unused computing resources with resource constraint devices. However, unbalanced task offloading in Social IIoT (SIIoT) might actually degrade the overall system performance, which is not desirable. In this paper, we propose an adaptive capacity task offloading solution for D2D-based social industrial IoT (ToSIIoT) which considers devices utilization ratio and strength of social relationships in order to improve resource utilization, increase QoS and achieve better task completion rate. The proposed approach consists of three aspects: social-aware relay selection in a multi-hop D2D communication context, choice of a resource-rich SIIoT device for task offloading, and adaptive redistribution of tasks. The paper proposes heuristic algorithms, as finding optimal solutions to the problems are NP-hard. Extensive experimental results show that the proposed ToSIIoT performs better than existing approaches in terms of utilization ratio, QoS violation, average execution delay, and task completion ratio.

(039) Social Support Significantly Mediates the Relationship Between Gender Role Nonconformity and Sexual Dysfunction

Abstract Introduction Uncertainty remains about the psychological and sexual implications of gender role nonconformity (GRNC), generally defined as men endorsing or performing femininity, and women endorsing or performing masculinity. Previous studies have demonstrated that variance in gender presentation is associated with negative psychological consequences. Homophobic stigmatization and internalized homophobia partially mediate this association, suggesting it is not the practice of GRNC that causes distress, but internal or external reactions to it. Objective Here, we test the hypothesis that people reporting higher levels of GRNC will have more sexual dysfunction, and that social support will mediate this relationship. Methods We analyzed data from the Nathan Kline Institute Rockland Sample. The sample we selected includes 781 subjects (age= 48.99 ± 17.42, sex= 67% female). All subjects completed the Sex Role Identity Scale (SRIS) and the Trauma Symptom Checklist (TSC-40). GRNC was quantified by SRIS questions-subjective GRNC was assessed using the question “How feminine/masculine do you think you are?”, behavioral GRNC was assessed with “How feminine/masculine do you act or behave?”, and perceived GRNC with “How feminine/masculine do you think you appear and come across to others?” A composite variable was created to assess overall GRNC. Sexual dysfunction was measured with the relevant TSC-40 subscale. Because the distribution of GRNC scores were not normally distributed, we performed a quantile regression analysis using the quantreg and mediation packages in R to understand the mediation effect of social support on the relationship between GRNC and sexual dysfunction, while controlling for sex, age, sexual orientation, and socioeconomic status. Results In the quantile regression model, GRNC significantly predicted sexual dysfunction (β = 0.86, 95%CI [0.52, 1.20]) and social support significantly predicted sexual dysfunction (β = -3.63, 95%CI [-4.30, -2.42]). In the mediation analysis, there was a significant mediation effect of social support on the relationship between GRNC and sexual dysfunction (β = 0.17, 95%CI [0.04, 0.31], p<0.001), controlling for sex, age, sexual orientation, and socioeconomic status. The mediation effect accounted for 16.56% (95%CI [0.05-0.28], p<0.001) of the variance in sexual dysfunction. Conclusions GRNC significantly predicts sexual dysfunction, and social support significantly mediates this relationship, accounting for a substantial percentage of its variance. These results suggest that although GRNC can be associated with negative psychological and sexual consequences, the impacts are significantly mediated by the quality and strength of social relationships. This supports previous literature suggesting that negative reactions to nonconformity, both personal and systemic, may account for decreased well-being in people exhibiting GRNC. Although the findings are limited by the nature of secondary analyses, these results highlight the clinical significance of understanding GRNC to better treat patients with sexual dysfunction and promote healthy sexual functioning for all individuals. Disclosure No

Mobile Social Recommendation Model Integrating Users’ Personality Traits and Relationship Strength under Privacy Concerns

Aiming at the problem of data sparsity, cold start, and privacy concerns in complex information recommendation systems, such as personalized marketing on Alibaba or TikTok, this paper proposes a mobile social recommendation model integrating users’ personality traits and social relationship strength under privacy concerns (PC-MSPR). Firstly, PC-MSPR focuses on specific personality traits, including openness, extraversion, and agreeableness, and their impacts on mobile users’ online behaviors. A personality traits calculation method that incorporates privacy preferences (PP-PTM) is then introduced. Secondly, a novel method for calculating the users’ relationship strength, based on their social network interactive activities and domain ontologies (AI-URS) is proposed. AI-URS divides the interactive activities into activity domains and calculates the strength of relationships between users belonging to the same activity domain; at the same time, the comprehensive relationship strength of users in the same domain, including direct relationships and indirect relationships, is calculated based on interactive activity documents. Finally, social recommendations are derived by integrating personality traits and social relationships to calculate user similarity. The proposed model is validated using empirical data. The results show the model’s superiority in alleviating data sparsity and cold-start problems, obtaining higher recommendation precision, and reducing the impact of privacy concerns regarding the users’ adoption of personalized recommendation services.

HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content

Popularity prediction of online content over social media platforms is a valuable and challenging issue, the core of which lies in how to capture predictive factors from available data. However, existing studies either treat each cascade independently, which neglects the correlation among different cascades, or lack a comprehensive consideration of user behavioral proximity and preference with respect to different messages. Motivated by the above observation, this article proposes a graph neural network-based framework named HeDAN (heterogeneous diffusion attention network), which comprehensively considers various factors affecting information diffusion to provide more accurate prediction results. Specifically, we first construct a heterogeneous diffusion graph with two types of nodes (user and message) and three types of relations (friendship, interaction, and interest). Among them, friendship reflects the strength of social relationships between users, interaction reflects the behavioral proximity between users, and interest reflects user preference for information. Next, a graph neural network model with a hierarchical attention mechanism is proposed to learn from these relations. Specifically, at the node level, we utilize the graph attention network to learn the subgraph structure and generate the representations of users and messages under each specific relationship. At the semantic level, we distinguish the importance of different nodes in different relations via the multi-head self-attention mechanism and fuse them into the final prediction representation. Extensive experimental results on three real diffusion datasets show the superior performance of HeDAN over the state-of-the-art baselines.

ANALISIS UNSUR MODAL SOSIAL TERHADAP PRODUKTIVITAS PETANI RUMPUT LAUT DI KECAMATAN SUPPA KABUPATEN PINRANG

Social capital refers to the strength of social relationships among the society which directly affects the productivity and wealthy of the society. This research focuses on certain Social capitals such as: participation in a network, reciprocity, and proactive action. The objective of this research are to determine what social capitals are implemented by the farmenrs and its effect towards the productivity of the seaweed in Suppa Pinrang. This research applied Qualitative-Quantitative research design. Qualitative method is administered to measure the social capital while the Quantitative to explain the productivity of the farmers. The result shows that the strongest social capital is Reciprocity and the others social capital are also positively affect the productivity of the seaweed at Suppa Pinrang Keywords: Social capital; farmer; productivity of seaweed

The Influence Mechanism of Social Interactions on Online Purchasing Intention of Mobile Social Users in a Low-Trust Business Environment

The success of a business or brand depends on how effectively mobile social interactions can be utilized to drive online consumption under the decentralized and low-trust virtual social space. This paper, constructing the influence mechanism of mobile social interactions on online purchasing intentions from the combination of information interactions and relationship interactions, performs analysis for exploratory factors, reliability and validity to test the formal scale and then examines the proposed hypotheses by structural equations. The empirical results show that four-factor variables of mobile social interactions, namely information value, the control of information dissemination, the strength of social relationships and the scope of social influence, have a direct positive effect on online purchasing intention with mobile social initial trust as a mediator. In addition, the product attributes and other external attributes play important moderating roles. These findings extend the research related to trust mechanisms in the new media environment and attempt to provide a theoretical basis and practical guidance for enterprises to better implement mobile social marketing strategies.

Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Collaborative mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this article, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: 1) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and 2) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its social network (SN) neighbors. We first formulate the recruitment as an integer linear program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: 1) level of expertise; 2) social relationship strength; 3) recruitment cost; and 4) recruiter’s confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on graph neural networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers’ search space and afterwards, exploiting a metaheuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world data set illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

Why do animal groups matter for conservation and management?

AbstractThe internationally recognized levels of conservation, namely ecosystems, species, and genes, have thus far served as important guidelines to determine how national and international laws should protect nature. However, a far ignored aspect of a species' life history in the legislation is its tendency to form social groups. Group members greatly depend on each other for survival and reproduction, and when the persistence of groups is threatened, so may the population as a whole. Humans affect groups through indirect activities, such as tourism, or directly by removing individuals through poaching, for example. These activities disturb groups in both predictable and unpredictable ways: destabilizing dominance hierarchies, changing the strength of social relationships, modifying cooperative interactions, reducing alloparental care, and altering social skills, among others. We propose that greater efforts must be undertaken first, to more thoroughly understand how our actions are affecting group dynamics in as many species as possible, and second, to adapt policies to reduce the negative effects of direct and indirect anthropogenic activities on group and population persistence.

Social Relationship Strength Modulates the Similarity of Brain-to-Brain Representations of Group Members.

Within our societies, humans form co-operative groups with diverse levels of relationship quality among individual group members. In establishing relationships with others, we use attitudes and beliefs about group members and the group as a whole to establish relationships with particular members of our social networks. However, we have yet to understand how brain responses to group members facilitate relationship quality between pairs of individuals. We address this here using a round-robin interpersonal perception paradigm in which each participant was both a perceiver and target for every other member of their group in a set of 20 unique groups of between 5 and 6 members in each (total N = 111). Using functional magnetic resonance imaging, we show that measures of social relationship strength modulate the brain-to-brain multivoxel similarity patterns between pairs of participants' responses when perceiving other members of their group in regions of the brain implicated in social cognition. These results provide evidence for a brain mechanism of social cognitive processes serving interpersonal relationship strength among group members.

Attractiveness of female sexual signaling predicts differences in female grouping patterns between bonobos and chimpanzees

Here we show that sexual signaling affects patterns of female spatial association differently in chimpanzees and bonobos, indicating its relevance in shaping the respective social systems. Generally, spatial association between females often mirrors patterns and strength of social relationships and cooperation within groups. While testing for proposed differences in female-female associations underlying female coalition formation in the species of the genus Pan, we find only limited evidence for a higher female-female gregariousness in bonobos. While bonobo females exhibited a slightly higher average number of females in their parties, there is neither a species difference in the time females spent alone, nor in the number of female party members in the absence of sexually attractive females. We find that the more frequent presence of maximally tumescent females in bonobos is associated with a significantly stronger increase in the number of female party members, independent of variation in a behavioural proxy for food abundance. This indicates the need to look beyond ecology when explaining species differences in female sociality as it refutes the idea that the higher gregariousness among bonobo females is driven by ecological factors alone and highlights that the temporal distribution of female sexual receptivity is an important factor to consider when studying mammalian sociality.

Positive Effects of Religion and Social Ties on theHealth of Former NFL Athletes.

This study explores the relationship between religious service attendance, social ties, and health among former NFL players, a population with relatively high levels of religious attendance who endure physically demanding occupations. Research shows that frequent religious service attenders tend to have better health, partly because of social connections formed through religious involvement. We analyzed a sample of 1029 former NFL players. Consistent with previous research, bivariate and multivariate OLS regression models show that frequent religious attenders have statistically significantly better self-rated health. However, this relationship is moderated by social ties. Respondents who scored lower on the social ties index exhibited a stronger significant relationship between frequent religious attendance and health; those scoring higher on the social ties index exhibited no relationship between frequent attendance and health. Future research should examine how benefits of religious attendance vary depending upon strength of social relationships.

A personalized trajectory privacy protection method

Trajectory data of sports or activities are usually collected and shared into social apps like Wechat moments, Sina weibo in public to provide health services and recommendation, while a large number of friends with weak ties in social circle will cause privacy leakage of users’ locations and life habits. To solve the problem, a personalized trajectory privacy protection scheme based on relationship strength called PTPP is proposed, the location obfuscation algorithm based on noise radius limiting geo-indistinguishability and location clustering is explored. Not only is privacy protected, but privacy budgets are controlled in fine grain according to relationship strength between users. Meanwhile, a hybrid calculation model of social relationship strength called HCM is proposed, which combines clustering and BP neural network and improve the reasonableness of social relationship strength. Finally, the availability and security of the PTPP algorithm are analyzed in the application scenarios of social networks. Analysis and the experimental results show that the method proposed could evaluate the strength of the relationship between users effectively and achieve personalized trajectory privacy protection.

Exploring the contextual transition from spinal cord injury rehabilitation to the home environment: a qualitative study

Study designExplorative qualitative study based on an interpretative phenomenological approach.ObjectivesThis study explored the possibility of transferring knowledge and skills from a spinal cord injury (SCI) unit to the home environment; the individual and structural factors that potentially influenced this transfer; and its compatibility with a meaningful everyday life.SettingHospital-based rehabilitation unit and community in Denmark.MethodsFourteen individuals with SCI were selected with maximum variation according to age, sex, marital status, and level of injury. In-depth, semi-structured interviews were conducted in the participants’ homes, 2–10 months after discharge from an SCI unit. Data analysis involved taking an interpretative phenomenological approach combined with a template analysis and applying the transfer of training theory to the discussion.ResultsTransitioning from the SCI unit to the home environment involved a multidimensional change of context in which most of the participants’ previous life roles had changed. This overarching theme had a decisive influence on: balancing loss and acceptance, facing external structural barriers, and the strength of social relationships when the knowledge and skills acquired at the unit were applied in a meaningful everyday life.ConclusionsTransition from the SCI unit to the home environment is influenced by a multidimensional change of context that may restrict the use of acquired skills post-discharge, provide distant prospects for tertiary health promotion, and aggravate the experience of loss in people with SCI. Maintaining relationships is a strong mediator for transferring skills and re-establishing a meaningful everyday life.

The Impact of Sheltering in Place During the COVID-19 Pandemic on Older Adults' Social and Mental Well-Being.

ObjectivesWe examined whether social isolation due to the COVID-19 shelter-in-place orders was associated with greater loneliness and depression for older adults, and, if so, whether declines in social engagement or relationship strength moderated that relationship.MethodsBetween April 21 and May 21, 2020, 93 older adults in the United States who had completed measures characterizing their personal social networks, subjective loneliness, and depression 6–9 months prior to the pandemic completed the same measures via phone interview, as well as questions about the impact of the pandemic on their social relationships.ResultsOlder adults reported higher depression and greater loneliness following the onset of the pandemic. Loneliness positively predicted depression. Perceived relationship strength, but not social engagement, moderated this relationship such that loneliness only predicted depression for individuals who became closer to their networks during the pandemic. For those who felt less close, depression was higher irrespective of loneliness.DiscussionThe COVID-19 pandemic negatively affected older adults’ mental health and social well-being in the short term. Potential long-term impacts are considered.

Correction to: Social security: social relationship strength and connectedness influence how marmots respond to alarm calls

Unfortunately, there was an error in the R script which led to the incorrect labeling of two social network measures.

Trust-based security routing mechanism in mobile social networks

Malicious and selfish behaviors represent a serious security threat against routing in mobile social networks (MSN). Due to MSN’s unique network characteristics, such as sociability, mobility and diversity, it is a challenge to design a misbehavior detection scheme in MSN. To improve the security in MSN routing, a trust-based security routing mechanism is presented in this paper, i.e., a malicious behavior detection mechanism with identity verification scheme. The main idea is to introduce the behavior trust and the identity trust to guide the routing. Firstly, we judge a node’s behavior based on both comprehensive trust and social relationship strength. And then, we forward messages according to the different measurement within and outside the friend groups. We also propose a distributed key management scheme which can issue, verify or revoke certificates based on the evaluation of the social relationship strength. We further improve the efficiency of the proposed mechanism by verifying the identity trust of the node. The simulation results indicate that the proposed routing mechanism can lower the impact of malicious behavior of nodes effectively and it has greater security performance than some classical routing algorithms do.

GEVR

In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users. Our study leverages a real-world dataset collected using the OutWithFriendz mobile app for group event planning, which contains 625 users and over 500 group events. We first develop a novel "social-based" group location prediction model, which adaptively applies different group decision strategies to groups with different social relationship strength to aggregate each group member's location preference, to predict where groups will meet. Evaluation results show that our prediction model not only outperforms commonly used and state-of-the-art group decision strategies with over 80% accuracy for predicting groups' final meeting location clusters, but also provides promising qualities in cold-start scenarios. We then integrate our prediction model with the Foursquare Venue Recommendation API to construct an event venue recommendation framework for groups of mobile users. Evaluation results show that GEVR outperforms the comparative models by a significant margin.