Behavior In Social Networks Research Articles

Power Indices in the Context of Social Learning Behaviour in Social Networks

This paper puts forward the concept of integrated power, synthetically measures the voters’ ability to influence the results of decision-making by influencing others through social learning, considering the interactions between decision-makers in social networks, and offers a method for measuring integrated power. Based on the theory and model of social learning, we analyze the influence of social learning on the voting process and power indices from the perspective of individuals’ professional level, position within the social network structure, relationship closeness, and learning efficiency. A measurement model of integrated power is constructed, and the variation in integrated power compared with that of the Banzhaf index is analyzed by numerical simulation. The results show that when the individual’s professional level is higher and closeness with neighboring decision-makers is greater, then the integrated power index is higher. An individual’s integrated power index may decrease when he/she changes from an isolated node to a nonisolated node, and then his/her integrated power will increase with the increases of neighbor nodes. Social learning efficiency can promote the integrated power of individuals with lower social impact and relationship closeness, but it is not beneficial for the core and influential members of the social network.

DEPLEST: A blockchain-based privacy-preserving distributed database toward user behaviors in social networks

Social networks record a significant amount of user behavior data every day. By analyzing this behavior data, companies or attackers use it for marketing or more questionable purposes. We propose a blockchain-based model to protect the privacy of users’ data in such big data environments. Traditional blockchain methods require too many resources for this task, so we propose a model that secures sensitive user information in a distributed blockchain and passes nonsensitive information through to the primary system in order to manage the blockchain size. Our DEPLEST algorithm performs these synchronization operations to keep local database storage and computational capacity within the limits of individual users’ devices. We also propose a consensus protocol for blockchain ledger maintenance that runs well on typical client systems and prove that this protocol has excellent Byzantine fault tolerance (BFT). Our experimental results show that DEPLEST meets the architectural and performance needs and that our consensus protocol outperforms the existing proof of work (PoW) and proof of stake (PoS) methods in this application.

PRINCIPLES OF SEARCHING AND SORTING OPTIMIZATION IN SOCIAL NETWORKS USING A MULTI-FACTOR ASSESSMENT SYSTEM

The analysis of social networks, which focuses on the relationship between social entities today is an area of active research. It is a set of tools for research, in particular, in combination with artificial intelligence methods such as machine learning, deep learning. The paper examined the current quality of the assessment of information in social networks, analyzed the methods of searching and sorting information in various social networks, as well as the process of providing recommendations to users. Social media data is an inexhaustible source of research and business opportunities. In general, social media data is information gathered from social networks that shows how users interact with content. Methods of improving search results for personalizing recommendations in social networks are given. These indicators and statistics provide an effective understanding of the strategy of behavior in social networks. The advantages and disadvantages of a multifactor assessment system are considered. The possible ways of integrating the combined system of evaluating information elements by the user to optimize search queries and filtering big data are identified.

No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior

The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.

A multidimensional model of knowledge sharing behavior in mobile social networks

PurposeA great number of people use mobile social networks (MSNs) to communicate, entertain, learn, search and get advice. Growth and survival of any community depends on the activities of its members in sharing information and knowledge. The purpose of this study is to assess the influential factors on knowledge sharing behavior in MSNs in different perspectives in a comprehensive manner.Design/methodology/approachA model of factors affecting knowledge sharing behavior in MSNs is proposed by applying the structural equation modeling and path analysis to data collected from a sample of users of a well-known MSN through a questionnaire.FindingsThis study supports the contributive aspects of trust and enjoying participation in sharing knowledge, while there is no significant correlation between perceived ease of use and knowledge sharing behavior in MSNs. Furthermore, intention to share knowledge can lead to actual behavior in MSNs environments.Practical implicationsThe results obtained here provide a grasp of factors that influence knowledge sharing in mobile communities which would promote enhanced contribution towards their online communities by MSNs administrators.Originality/valueA four-dimensional comprehensive model consisting of social, psychological, cultural and technological perspectives in one package is proposed here for knowledge sharing behavior in MSNs. Such a comprehensive perspective is overlooked in the existing literature.

Veterans with recent substance use and aggression: PTSD, substance use, and social network behaviors.

Violence is a salient concern among veterans, yet relationships between psychiatric comorbidity, social networks, and aggression are poorly understood. We examined associations between biopsychosocial factors (substance use, posttraumatic stress disorder [PTSD], and social network behaviors) with aggression. We recruited veterans endorsing past-year aggression and substance use (N = 180) from Department of Veterans Affairs outpatient treatment clinics. Main and interaction effects between probable PTSD, substance use, social network violence and substance use, and veteran violence were examined with negative binomial regressions-specifically, physical aggression toward a relationship partner (PA-P), physical injury of a partner (PI-P), physical aggression toward nonpartners (PA-NP), and physical injury of nonpartners (PI-NP). Alcohol use yielded consistent main effects. PTSD and social network violence demonstrated main effects for PA-NP and PI-NP. PTSD and social network violence interacted to predict PA-P such that social network violence appeared salient only in the context of PTSD. PTSD was associated with PI-P, PA-NP, and PI-NP in social network substance use models. In the PA-P model including social network substance use, veterans with PTSD reported greater PA-P in the context of greater social network substance use, whereas veterans without PTSD endorsed PA-P concurrent with greater alcohol frequency. For PI-P, PTSD interacted with alcohol to predict a greater likelihood of partner injury in the context of social network substance use. Investigated variables demonstrated unique associations within the context of specific relationships and the severity of behaviors. Overall, the findings underscore the importance of biopsychosocial models for understanding veteran violence. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

An Examination of Factors That Influence Social Networking Community Participation Among Millennials

This study investigates main and moderating factors that influence Millennials' intention to participate in a social networking community (SNC). The authors modified the unified theory of consumers' acceptance and use of technology (UTAUT2) to incorporate six main and two moderating factors to explain Millennials' SNC participation intention. By considering the implications of the unique characteristics of Millennials on their social networking behavior, the authors' model is better suited to answer what drives these tech-savvy individuals to participate in a SNC via such sites as Facebook. Specifically, the authors find that hedonic motivation, trust in technology, trust in community, and social influence are significant factors in influencing Millennials' SNC participation intention, with hedonic motivation being the most influential factor. In addition, gender and educational background moderate the main effects of these determinants in different manner. Theoretical and practical implications of these findings are discussed.

Gene expression in the social behavior network of the wire-tailed manakin (Pipra filicauda) brain.

The vertebrate basal forebrain and midbrain contain a set of interconnected nuclei that control social behavior. Conserved anatomical structures and functions of these nuclei have now been documented among fish, amphibians, reptiles, birds and mammals, and these brain regions have come to be known as the vertebrate social behavior network (SBN). While it is known that nuclei (nodes) of the SBN are rich in steroid and neuropeptide activity linked to behavior, simultaneous variation in the expression of neuroendocrine genes among several SBN nuclei has not yet been described in detail. In this study, we use RNA-seq to profile gene expression across seven brain regions representing five nodes of the vertebrate SBN in a passerine bird, the wire-tailed manakin Pipra filicauda. Using weighted gene co-expression network analysis, we reconstructed sets of coregulated genes, showing striking patterns of variation in neuroendocrine gene expression across the SBN. We describe regional variation in gene networks comprising a broad set of hormone receptors, neuropeptides, steroidogenic enzymes, catecholamines and other neuroendocrine signaling molecules. Our findings show heterogeneous patterns of brain gene expression across nodes of the avian SBN and provide a foundation for future analyses of how the regulation of gene networks may mediate social behavior. These results highlight the importance of region-specific sampling in studies of the mechanisms of behavior.

Role of Social Network Sexual Norms and Behaviors on the HIV Sexual Risk Behaviors of People Who Inject Drugs in HPTN 037.

This study examined the effect of social network descriptive sexual norms and behaviors on the sexual behaviors of people who inject drugs (PWID). Data from HPTN037 of 232 PWID (egos) and 464 network members (alters) were used in multilevel multivariate logistic regression models. Egos whose alters reported multiple sex partners had greater odds of multiple sex partners (aOR 2.20, 1.13-4.29). Egos' norms of condomless sex with primary (aOR 2.67, 1.15-6.17) and casual (aOR 2.38, 1.01-5.59) partners and egos' norms of giving (aOR 5.52, 1.87-16.25) and receiving (aOR 7.38, 1.34-40.66) money/drugs for sex were associated with the egos' respective behaviors. History of sex between an ego and alter was not associated with increased influence of alters' norms and behaviors on egos' sexual behavior. Findings provide support for developing interventions that target descriptive norms and selective network behavioral characteristics to decrease sexual HIV risk behavior among PWID.

Social Media Behaviors: A Study of International Students in BBA Program in International Business

The purpose of this study was to examine social media network behaviors of international students in BBA program at a selected government university. Thirty-three students who enrolled in ‘principles of management’ course in the second academic year of 2019 were requested to fill out the survey questionnaire through the Google classroom. Only twenty-two students from three different countries agreed to complete the questionnaire and returned with completion. Results showed that Instagram was the most frequently used social media application among this group of students. Additionally, participants reported that the major purpose of using social media was ‘follow people’, and they approximately spent 4-6 hours a day in using social media. Chi-square correlation with Cramer’s V technique indicated a statistically significant association between nationality and length of time in using social media within a day at a significant level of 0.05.

Final Outcomes Report: Rochester Institute of Technology: Advance RIT

The Rochester Institute of Technology ADVANCE Institutional Transformation grant seeks to improve conditions for women STEM faculty with a particular emphasis on women of color and women who are deaf and hard of hearing. Utilizing the theoretical foundation of Social Networking Behaviors, they have organized their initiatives in areas consistent with their theory. The first area, Structural, will include the analysis, modification, and clarification of policy and practice such that there is policy alignment and awareness throughout the system. The second area, Human Resources, will include professional development strategies, targeted mentoring, including mentoring specifically designed to address the unique concerns of women who are members of underrepresented groups (e.g., women of color, women who are deaf and hard or hearing), and work/life balance initiatives. The third area, Political/Strategic, will include purposeful methods to engage campus leaders in the process of institutional transformation including integration of proposed initiatives into existing campus structures and selective data-driven outreach to leader groups. The final area, Symbolic, will include broad within campus dissemination of the activities and outcomes of the initiatives as well as deliberate, high-level, and public endorsements of the ADVANCE initiatives and goals. Evaluation is thoughtfully intertwined throughout the programming allowing a robust test of the usefulness of this approach to institutional transformation. Their initiatives are also firmly rooted in their understanding of their particular institutional context which was revealed, in part, through the use of NSF ADVANCE Catalyst funds. All indications suggest that the institution is poised for the kinds of transformations sought through this grant including; strong letters of support from the administration, specific intentions to address gender equity in their most recent strategic plan, and clear integration of proposal initiatives into the existing university structures. The strength of the PI team, rigor of their social science project and well-conceived evaluation plan make the broad dissemination of their results highly probable increasing the broader impacts of the grant.

Do migrant social networks shape political attitudes and behavior at home?

What is the role of international migrants and, more specifically, of migrant networks in shaping political attitudes and behavior in migrant sending countries? We propose that migration might change social norms for political participation, while it may also improve knowledge about better quality political institutions. Hence, international migration might increase political awareness and participation both by migrants and by other individuals in their networks. To test this hypothesis, we use detailed data on different types of migrant networks, namely geographic, kinship and chatting networks, as well as several different measures of political participation and electoral knowledge – namely, self-reports, behavioral and actual voting measures. These data were purposely collected around the time of the 2009 elections in Mozambique, a country with substantial emigration to neighboring countries and with one of the lowest political participation rates in the southern Africa region. The empirical results show that the number of migrants an individual is in close contact through regular chatting within a village significantly increases political participation of residents in that village – more so than family links to migrants. Our findings are consistent with both improved knowledge about political processes, and increased intrinsic motivation for political participation being transmitted through migrant networks. These results are robust to controlling for self-selection into migration as well as endogenous network formation. Our work is potentially relevant for the many contexts of South-South migration where both countries of origin and destination are imperfect political systems. It shows that even in this context there may be domestic gains arising from international emigration.

Vital nodes extracting method based on user's behavior in 5G mobile social networks

In mobile social networks, extracting the most powerful individuals to disseminate information in the network is attracting the attention of many researchers. Identifying influential nodes in Mobile Social Networks (MSNs) helps to increase the efficiency of bandwidth in wireless communication by leveraging cellular links to device-to-device communications. Recently, numerous techniques have been proposed from different perspectives, each with its particular advantages and weaknesses. In this paper, Node Willingness and Influence (NWI) algorithm is proposed to extract the most powerful spreaders in 5G MSNs, which considers the willingness and influence of the node to disseminate information in the network through its neighbors and 2-step neighbors. Firstly, the Influence (In) of a node is calculated based on the nodes' willingness to share contents with others. Then, based on node degree and neighbors node degree; and nodes strength and neighbors node strength, the Weighted Strength Degree (WSD) and the Clustering Impact Coefficient (CIC) of a node is determined. Finally, the importance of a node based on its willingness to propagate information in the network is done by accounting the Influence (In) and CIC of the node. The temporal evolution graph and time-aggregated graph models are used to capture the topology dynamics of the mobile social networks. Also, Susceptible-Infected-Recovered (SIR) model is used to evaluate the performance of NWI to disseminate information in real-world networks. Results show the effectiveness of the proposed method to extract important nodes in MSNs.

Studying the relationship between activity participation, social networks, expenditures and travel behavior on leisure activities

In the context of an increasing interest in understanding travel for non-mandatory activities, such as recreation and socializing, this work focuses on studying the relationships between activity participation, social networks, and expenditures in daily travel patterns associated with leisure activities in order to understand people’s strategies for performing activities in daily life. Using a 7-day time use diary from a resident sample of Concepcion, Chile, along with information about people’s socio-demography, social network and expenditure behavior, structural equations models were estimated to study the role of social networks on people’s space–time and monetary patterns. The results suggest a positive relationship between people’s interaction with their social networks, their expenditure levels, and their space–time activity patterns. The analysis adds empirical evidence towards a better understanding of people’s decision-making processes by using a time use and a social networks approach. The model results reveal that out-of-home leisure time has a strong impact on the interactions with alters and monetary expenditures. In this context, “with whom” and how much time someone spends doing a specific activity act as key intermediary dimensions to explain leisure activity participation and travel behavior.

Site-specific effects of aromatase inhibition on the activation of male sexual behavior in male Japanese quail (Coturnix japonica)

Aromatization within the medial preoptic nucleus (POM) is essential for the expression of male copulatory behavior in Japanese quail. However, several nuclei within the social behavior network (SBN) also express aromatase. Whether aromatase in these loci participates in the behavioral activation is not known. Castrated male Japanese quail were implanted with 2 subcutaneous Silastic capsules filled with crystalline testosterone and with bilateral stereotaxic implants filled with the aromatase inhibitor Vorozole targeting the POM, the bed nucleus of the stria terminalis (BST) or the ventromedial nucleus of the hypothalamus (VMN). Control animals were implanted with testosterone and empty bilateral stereotaxic implants. Starting 2 days after the surgery, subjects were tested for the expression of consummatory sexual behavior (CSB) every other day for a total of 10 tests. They were also tested once for appetitive sexual behavior (ASB) as measured by the rhythmic cloacal sphincter movements displayed in response to the visual presentation of a female. CSB was drastically reduced when the Vorozole implants were localized in the POM, but not in the BST nor in the VMN. Birds with implants in the BST took longer to show CSB in the first 6 tests than controls, suggesting a role of the BST in the acquisition of the full copulatory ability. ASB was not significantly affected by aromatase blockade in any region. These data confirm the key role played by the POM in the control of male sexual behavior and suggest a minor role for aromatization in the BST or VMN.

Social Capital Formation Through a Polish LETS: Challenging the Presumed Merits of Local Currencies

The study examines the mechanisms of social capital formation in a Polish local exchange trading system to determine whether the interpersonal interactions connected with transactions influence the cooperative attitudes and behaviors of system members. The stochastic actor-oriented model for the coevolution of social network and individual behavior is utilized. Simultaneously analyzing the dynamics of the network of social ties associated with transactions and the change in a proxy for cooperative behavior does not provide evidence for the spreading of cooperative attitudes. Neither exposure to good-example providers nor the high activity of members in the system enhances their cooperative behavior. On the contrary, network formation is conditioned by the cooperative attitudes of its members.

Psychological Gender Express via Mobile Social Network Activities: An Experimental Study on a Gay Network Data

It is well known that the social network users’ behavior can reflect their personal mental profiles. Therefore, in this paper, we conducted an experiment to study users’ gender identity through a mobile social network. Gender identity is the personal sense of one’s own gender, which can be the same or different from their sex assigned at birth, that is, one’s innermost concept of self as male, female, a blend of both, or neither. In general, a person’s psychological gender cannot be observed from the external characteristics of people. And even if some user’s attributes such as physical gender and age can be found in social networks, it is difficult to find out the psychological gender of users. Apart from the fact that it will be hidden as the user’s privacy, the main reason is that users’ behaviors in social networks have no direct relationship with psychological gender. In this paper, we choose a special mobile social network to get around these limitations. Specifically, this paper is based on a real-world large gay social network of more than 10 000 000 users. In this mobile social network, we can indirectly mark the psychological gender of users by their sexual orientation. We collect 1000 users, including their characters, followers, followees and the same data for their followers and followees. Then, we examine an extensive set of features that can be extracted from the data set and train some classifiers to automatically identify the user’s psychological gender. In the special mobile social network, a user’s psychological gender is related to his characteristics, which include his personal characteristics, dating preference, and so on. This paper mainly analyzes users’ personal characteristics, triads, and triadic evolution to infer their psychological gender. The experiments show that all these features are indeed effective in the classification, and even the features considered in previous studies have different implications with gender identity on other social networks. And it turns out that psychological gender as a kind of privacy can be leaked by these unintentional behaviors, and so we give some practical suggestions at the end of this paper to protect users’ privacy.

The Analysis of Sub-Communities Behavior in Social Networks

Clique relations are useful in understanding the dynamics of a wide range of social interactions. One application of studying clique relations involves studying how such “detection of abnormal cliques’ behaviors” can be used to detect sub-communities’ behaviors based on information from Online Social Networks (OSNs).In social networks, a clique represents a sub-group of the larger group in which every member in the clique is directly associated with every other member in the clique. Those cliques often possess a containment relation with each other where large cliques can contain small size cliques. Thus, finding the extent of the clique, or the maximum clique is an important research questions. In our approach, we evaluated adding the weight factor and integrating graph theory to clique algorithm in order to derive more data about the clique. In this regard clique activities are not like those in group discussions where an activity is posted by one user and is visible by all others. Our algorithm calculates the overall weight of the clique based on individual edges. Users post frequent activities. Their clique members, just like other entities, may or may not interact with all those activities.

Correlating Formal Assessment with Social Network Activity within a Personal Learning Environment

Social networks have undoubtedly penetrated into our daily life, in such a degree that educational life could not avoid this effect, as proven by the many education-oriented social networks that have emerged. The education-oriented social network environment named HOU2LEARN, used by the Hellenic Open University, is one of these networks, providing a valuable source of data about students' networking behavior and their tensions. This article aims at contributing to the investigation of possible linkages between social network behavior and student performance as assessed within a formal learning environment. The article is focused on analyzing data and performing statistical correlations from two consecutive and recent academic years in a computer science course, attempting to reach robust conclusions. Although the research question remains the same: Is there a relationship between grades and social activity within the social network? This article is supported by increased sampling and data, providing concrete and intriguing answers, thus setting the pillars for further research goals regarding contemporary and smart learning environments.

Implicit User Trust Modeling Based on User Attributes and Behavior in Online Social Networks

In this paper, we present a new user trustworthiness estimation model for social networks (SN), whereas most of existing researches have been focused on the user-user/item relationship trustworthiness estimation. Users share information of their interest on various social media without their trustworthiness verification. Therefore, SN are susceptible to malicious users for misinformation spreading. In SN, the original information source is generally unknown and the user who is sharing the contents is the only known information about the source. Therefore, the user's trustworthiness is an effective criterion for SN content's trustworthiness estimation. However, the users are unable to identify trustworthy/untrustworthy users, and the existing user-user/item relationship models do not provide user trustworthiness information. Our proposed model provides a systematic way to assess the user trustworthiness based on user attributes and interaction behavior. The proposed model is helpful to avoid the trust sparsity (implicit trust model), trust subjectivity (user's objective/collective trustworthiness estimation model) and cold-start user's trustworthiness (user's attributes-based trust modeling) problems. We employ friends-recommendation (FR) as an exemplary application to evaluate the performance of our proposed model in trust-aware recommendations. Simulation results illustrate that our trust-aware FR model outperformed the existing trust and FR models.