Behavior In Social Networks Research Articles

Female zebra finches are more vulnerable to social isolation stress than males: Involvement of the nonapeptide system.

In group-living animals, chronic juvenile social isolation stress (SIS) can profoundly affect behavior and neuroendocrine regulation. However, its impact on social behavior in avian species, particularly regarding sex-specific neural circuit differences, remains underexplored. This study focused on zebra finches, a species known for its social clustering and cognitive abilities, to elucidate these influences. Results indicated that SIS significantly increased plasma corticosterone levels in females but not in males, suggesting a heightened stress response and susceptibility in females. Additionally, SIS disrupted sociality and flocking behavior in both sexes, with more severe impairments in social recognition observed in females. Mesotocin (MT) levels in the lateral septum of both sexes and in the ventromedial hypothalamus of females were found to mediate the SIS effect, while vasotocin (VT) levels within the social behavior network remained unchanged. Pharmacological interventions confirmed the critical role of MT in reversing SIS-induced impairments in sociality, flocking behavior, and social recognition, particularly in females. These findings highlight unique nucleus- and sex-dependent variations in MT and VT regulation, providing novel insights into the mechanisms governing avian social behavior. This study advances our understanding of the independent evolutionary pathways of neural circuits and neuroendocrine systems that modulate social behaviors across different taxonomic groups.

Mediating Impact of Work Engagement and Moderating effect of Organization culture on the Relationship between Pro-social Motivation Social Media, Social Networking and Organizational Citizenship Behavior

The study aims to investigate effect of work engagement, pro-social motivation, social media, social networking and organization culture on organizational citizenship behavior. This study has used partial least square structural equation modeling to examine the construct hypotheses. The finding suggests the significant positive direct relationship between pro-social motivation, work engagement, social media, social networking and organizational citizenship behavior. In addition, work engagement positively mediated the relationship between pro-soical motivation, social media, social networking and organizational citizenship behavior. Besides, this research also found the significant moderation of organization culture between pro-social motivation, social media, social networking and work engagement. Discussion limitation and suggestion for future research are also provided.

Virtual connections: a multidisciplinary study on behavior in social networks

This article investigates the impact of social networks on various aspects of contemporary society through a multidisciplinary approach. A theoretical review with a mixed approach was used which, after an evaluation of relevance, led to the identification of 21 articles that make up the research. Exploring the relationship between patterns of social network use and their effect on mental health, it is found that active interaction promotes psychological well-being, while passive consumption is linked to negative feelings such as loneliness and envy. In addition, we examine how these platforms influence identity formation and public opinion, highlighting their role in creating "echo chambers" that can intensify social polarization. It is also noted that while social networks enhance global connectivity, they can compromise the quality of interpersonal relationships. Nevertheless, they facilitate the formation of communities of support and solidarity, particularly in groups with common interests, contributing to community cohesion. This study underlines the duality of social networks as spaces of opportunity and challenge, highlighting the need for a conscious use to foster individual well-being and social cohesion.

Assessment of the main features of the model of dissemination of information in social networks

<span lang="EN-US">Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.</span>

Development of policy measures for diffusing human pro-environmental behavior in social networks—Computer simulation of a dynamic model of mutual learning

Pro-environmental behavior does not diffuse sufficiently in society. Is there a way to enhance the degree of people's pro-environmental behavior? This study aims to develop a dynamic model of mutual learning in social networks to simulate the diffusion of pro-environmental behavior and to search for promising policies for promoting it. This study considers two policy measures: enhancing pro-environmental behavior of target people and changing the learning patterns of target people. The people targeted for intervention are determined by random selection, selection in descending order of degree centrality, and selection in descending order of eigenvector centrality. Centralities measure an influence of a node on other nodes through a network, based on the number of direct or indirect links. An interesting finding is that changing individual learning patterns is much more effective for enhancing the degree of pro-environmental behavior in social networks than trying to directly enhance its degree. In addition, selection of target people based on the centralities is more influential in encouraging environmentally friendly behavior than random selection, particularly in the policy of changing learning patterns. Multiplier effects are also measured: the ratio of the net increase in the number of people who enhance their degree of pro-environmental behavior at the end of a certain number of time steps beyond business as usual to the number of people intervened. Multiplier effects are always positive when learning patterns are changed. Six potential approaches to changing learning patterns are discussed: persuasion, reputation, competition, awareness of economic returns, information provisioning, and education.

A novel regularized weighted estimation method for information diffusion prediction in social networks

In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one of the leading fields in social network analysis. In this context, users are influenced by other users in the social network, such as their friends. User behavior is analyzed using several models designed for information diffusion modeling and prediction. In this paper, first, the problem of estimating the diffusion probabilities for the independent cascade model is studied. We propose a method for estimating diffusion probabilities. This method assigns a weight to each individual diffusion sample within a network. To account for the different effects of diffusion samples, several weighting schemes are proposed. Afterward, the proposed method is applied to real cascade datasets such as Twitter and Digg. We try to estimate diffusion probabilities for the independent cascade model considering the continuous time of nodes’ infections. The results of our evaluation of our methods are presented based on several datasets. The results show the high performance of our methods in terms of training time as well as other metrics such as mean absolute error and F-measure.

Comparative analysis of gonadal hormone receptor expression in the postnatal house mouse, meadow vole, and prairie vole brain

The socially monogamous prairie vole (Microtus ochrogaster) and promiscuous meadow vole (Microtus pennsylvanicus) are closely related, but only prairie voles display long-lasting pair bonds, biparental care, and selective aggression towards unfamiliar individuals after pair bonding. These social behaviors in mammals are largely mediated by steroid hormone signaling in the social behavior network (SBN) of the brain. Hormone receptors are reproducible markers of sex differences that can provide more information than anatomy alone and can even be at odds with anatomical dimorphisms. We reasoned that behaviors associated with social monogamy in prairie voles may emerge in part from unique expression patterns of steroid hormone receptors in this species, and that these expression patterns would be more similar across males and females in prairie than in meadow voles or the laboratory mouse. To obtain insight into steroid hormone signaling in the developing prairie vole brain, we assessed expression of estrogen receptor alpha (Esr1), estrogen receptor beta (Esr2), and androgen receptor (Ar) within the SBN, using in situ hybridization at postnatal day 14 in mice, meadow, and prairie voles. We found species-specific patterns of hormone receptor expression in the hippocampus and ventromedial hypothalamus, as well as species differences in the sex bias of these markers in the principal nucleus of the bed nucleus of the stria terminalis. These findings suggest the observed differences in gonadal hormone receptor expression may underlie species differences in the display of social behaviors.

Effect of social media usage on job security through social media disorder and networking behavior: a serial mediation mechanism

Purpose The usage of social media at the workplace has become an undeniable reality, yet the role of social media use (SMU) in job-related outcomes is still unclear. This study uncovers a chain process through which SMU may strengthen job security perception of employees through social media disorder (SMD) and networking behavior.Design/methodology/approach This quantitative study used ratings of 197 Emirati students enrolled in a higher education institution located in United Arab Emirates (UAE). The respondents were professionals serving in different public and private organizations in UAE.Findings The statistical results supported a significant serial mediation of SMD and networking behavior between SMU and job security perceptions of employees.Practical implications This study offers implications for employees and their supervisors about the usage of social media for strengthening their perceptions of job security.Originality/value This study contributed to the existing stream of research on SMU to explain a chain process through which employees may benefit from social media to strengthen their perceptions of job security.

Vasotocin but not isotocin is involved in the emergence of the dominant-subordinate status in males of the weakly electric fish, Gymnotus omarorum

The establishment of the dominant-subordinate status implies a clear behavioral asymmetry between contenders that arises immediately after the resolution of the agonistic encounter and persists during the maintenance of stable dominance hierarchies. Changes in the activity of the brain social behavior network (SBN) are postulated to be responsible for the establishment and maintenance of the dominant-subordinate status. The hypothalamic nonapeptides of the vasopressin (AVP) and oxytocin (OT) families are known to modulate the activity of the SBN in a context-dependent manner across vertebrates, including status-dependent modulations. We searched for status-dependent asymmetries in AVP-like (vasotocin, AVT) and OT-like (isotocin, IT) cell number and activation immediately after the establishment of dominance in males of the weakly electric fish, Gymnotus omarorum, which displays the best understood example of non-breeding territorial aggression among teleosts. We used immunolabeling (FOS, AVT, and IT) of preoptic area (POA) neurons after dyadic agonistic encounters. This study is among the first to show in teleosts that AVT, but not IT, is involved in the establishment of the dominant-subordinate status. We also found status-dependent subregion-specific changes of AVT cell number and activation. These results confirm the involvement of AVT in the establishment of dominance and support the speculation that AVT is released from dominants' AVT neurons.

Deep convolutional neural network based real-time abnormal behavior detection in social networks

In this study, we introduce a novel real-time approach for detecting abnormal behaviors in online social networks, leveraging both dynamic user activities and their associated profiles through Convolutional Neural Networks (CNN). Social networks, which serve as hubs for communication, discussion, and multimedia sharing, amass a plethora of user data. This vast reservoir of information, while invaluable for genuine interactions, is susceptible to exploitation by malicious entities. Despite numerous previously proposed detection methods, the challenge of ensuring security persists. Our distinctive framework, rigorously tested using an extensive dataset sourced from the web and executed via the CNN toolbox in MATLAB, consistently demonstrates an impressive 99.9% classification accuracy. This achievement not only underscores the method's effectiveness but also marks a significant stride towards proactively identifying and neutralizing potential threats in digital social platforms.

Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Steroid hormone-mediated regulation of sexual and aggressive behaviour by non-genomic signalling

Sex and aggression are well studied examples of social behaviours that are common to most animals and are mediated by an evolutionary conserved group of interconnected nuclei in the brain called the social behaviour network. Though glucocorticoids and in particular estrogen regulate these social behaviours, their effects in the brain are generally thought to be mediated by genomic signalling, a slow transcriptional regulation mediated by nuclear hormone receptors. In the last decade or so, there has been renewed interest in understanding the physiological significance of rapid, non-genomic signalling mediated by steroids. Though the identity of the membrane hormone receptors that mediate this signalling is not clearly understood and appears to be different in different cell types, such signalling contributes to physiologically relevant behaviours such as sex and aggression. In this short review, we summarise the evidence for this phenomenon in the rodent, by focusing on estrogen and to some extent, glucocorticoid signalling. The use of these signals, in relation to genomic signalling is manifold and ranges from potentiation of transcription to the possible transduction of environmental signals.

Urban and rural male song sparrows (Melospiza melodia) differ in territorial aggression and activation of vasotocin neurons in response to song challenge

When living in urban habitats, ‘urban adapter’ species often show greater aggression toward conspecifics, yet we do not understand the mechanisms underlying this behavioral shift. The neuroendocrine system regulates socio-sexual behaviors including aggression and thus could mediate behavioral responses to urbanization. Indeed, urban male song sparrows (Melospiza melodia), which are more territorially aggressive, also have greater abundance of the neuropeptide arginine vasotocin (AVT) in nodes of the brain social behavior network. Higher abundance of AVT could reflect long-term synthesis that underlies baseline territoriality or short-term changes that regulate aggression in response to social challenge. To begin to resolve the timeframe over which the AVT system contributes to habitat differences in aggression we used immediate early gene co-expression as a measure of the activation of AVT neurons. We compared Fos induction in AVT-immunoreactive neurons of the bed nucleus of the stria terminalis (BSTm) and paraventricular nucleus of the hypothalamus (PVN) between urban and rural male song sparrows in response to a short (< 5 min.) or long (> 30 min.) song playback to simulate territorial intrusion by another male. We found that urban males had a higher proportion of Fos-positive AVT neurons in both brain regions compared to rural males, regardless of the duration of song playback. Our results suggest that AVT neurons remain activated in urban males, independently of the duration of social challenge. These findings that Fos induction in AVT neurons differs between rural and urban male song sparrows further implicate this system in regulating behavioral responses to urbanization.

Assessment of users' behavior in Lithuanian online communities.

Online communities are gaining importance in modern society by actively structuring public opinion and initiating discussions about various socio-economical issues. As information and communication technologies advance, the online communities are confronted with novel technological and societal hurdles (the spread of misinformation, lack of active participation). To boost their efficacy and productivity, it's crucial to enhance our understanding of user behavior, communication avenues and potential future trends of development. Online platforms serve a function beyond simply sharing information or knowledge; they act as influential social networks affecting various societal sectors, including politics, culture and the economy. There is a need to recognize online communities not as static entities, but as dynamic, evolving systems of collective intelligence. A representative quantitative study was carried out between 1 to 30 of October 2022 through direct, in-person interviews conducted at the respondent's residence (known as an Omnibus survey). The sample of respondents is representative of the entire population of Lithuania regarding essential socio-demographic characteristics. By thoroughly analyzing data gathered from a comprehensive quantitative study, the study raises awareness of the issues surrounding online communities, while also shedding light on social networking behavior within these virtual spaces. Despite the multitude of challenges inherent to virtual communication, there remains a significant knowledge gap in comprehending general user behavior within these online communities. The current research aims to bridge this gap by investigating user behavior in Lithuanian online communities.

Collaborative Diffusion Model of Information and Behavior in Social Networks

Collaborative Diffusion Model of Information and Behavior in Social Networks

Context-dependent activation of a social behavior brain network during learned vocal production.

Neural activation in brain regions for vocal control is social context dependent. This context-dependent brain activation reflects social context-appropriate vocal behavior but has unresolved mechanisms. Studies of non-vocal social behaviors in multiple organisms suggest a functional role for several evolutionarily conserved and highly interconnected brain regions. Here, we use neural activity-dependent gene expression to evaluate the functional connectivity of this social behavior network within zebra finches in non-social and social singing contexts. We found that activity in one social behavior network region, the medial preoptic area (POM), was strongly associated with the amount of non-social undirected singing in zebra finches. In addition, in all regions of the social behavior network and the paraventricular nucleus (PVN), a higher percentage of EGR1 expression was observed during a social female-directed singing context compared to a non-social undirected singing context. Furthermore, we observed distinct patterns of significantly correlated activity between regions of the social behavior network during non-social undirected and social female-directed singing. Our results suggest that non-social vs. social contexts differentially activate this social behavior network and PVN. Moreover, neuronal activity within this social behavior network, PVN, and POM may alter context-appropriate vocal production.

Edited Pictures in Social Network and Borderline Personality Organization: Focusing on Shame, Guilt, Social Comparison, and Body Image

The habit to share edited pictures through social networks have been reported in young people and associated to psychological vulnerabilities. The research addresses the effects of the exposure to edited pictures on guilt, shame, social comparison tendencies, body image dissatisfaction. Moreover, the study assesses the mediating role of borderline organization on perceived identification and attractiveness in edited and original personal pictures.&#x0D; We asked seventy young women to rate perceived identification and attractiveness in their original and edited pictures. Subjects report higher identification in the original picture compared to the edited ones; attractiveness does not change between original and edited pictures, but high identification in edited ones negatively affects the score of attractiveness in original versions. They report negative associations between the level of identification and attractiveness in the original picture and dysphoria, guilt and body image. Identification and attractiveness in personal pictures are influenced by psychological vulnerabilities. Eventually, borderline organization features mediate the association between guilt and body image satisfaction with identification and attractiveness respectively. Furthermore, psychopathological features might affect individual self-perception and behavior in social network.

Rumor detection on social networks focusing on endogenous psychological motivation

Emerging information acquisition methods represented by social networks are increasingly popular. The freedom and concealment make social media become the main platform for rumor spreading. Rumor detection has attracted the attention of both academia and industry, and a series of rumor detection methods have emerged. However, existing methods focus on modeling exogenous features such as text content and user characteristics, while ignoring the endogenous psychological motivation of users. Sociological and psychological researches on rumor have demonstrated the correlation between the endogenous psychology of users and their behavior in social networks. Therefore, this study focuses on the intrinsic psychological motivation of users, probes the psychological changes of users after they are exposed to rumors from the perspectives of active sharing and passive response, analyzes and judges user behaviors to explore an efficient rumor detection method. Among them, active sharing behavior is based on the investigation of social users’ motivation, decision making and goal setting, and is related to users’ motivation to actively participate in content by creating posts and so on. Passive response, which is closely related to personal emotions originating from the influence of external factors, is a study of users’ cognitive processes and is associated with the way they handle information without active participation. Experiments demonstrate the superiority of our proposed method in rumor detection tasks, improving accuracy by 2.1% over the current baseline on the Twitter16 dataset and enabling early detection of rumors as they emerge.

Participation in Polish Local Elections: Sheer Rational Choice or Social Embeddedness Fate?

In this article, a modest attempt is undertaken to connect the rational choice theory mechanism with the sociological theory of voting, as efforts to juxtapose both theories are still rare, although some cited accomplishments offer interesting insights. Our dependent variable is participation in a local election in Poland. The local government elections in Poland seem to offer a setting particularly well-suited for a test of the rational choice hypothesis and its relation to sociological explanations of voting behavior. The essential contribution of the article derives from our conviction that social embeddedness and rooting into the social milieu and local community matter for both the cost of participating in a local election and the cost of deciding whether to participate. In the world of post-cleavage politics, individualization of political behavior and involvement in (new) social networks play an increasingly important role. The general finding can be summarized as follows: the social embeddedness of citizens does enhance the probability of participation in an election; however, it does not substitute for the impact of rational motivation factors, nor does it significantly decrease the latter’s net impact on our dependent variable.

Depression Detection Using Machine Learning

Abstract: Social media platforms like Facebook, Twitter, Instagram, and Reddit have had a profound and lasting impact on our society. They have increased connectivity among people and allowed individuals to create a digital persona. While social media has many positive aspects, there are also undeniable drawbacks. Recent research has shown a link between excessive social media use and higher levels of depression. The objective of this project is to identify depression by analysing audio, text, or recordings. The study utilizes machine learning techniques to detect potential signs of depression in users based on their network behaviour and tweets. To accomplish this, classifiers were trained and tested using features extracted from users' activities on social media networks and their tweets. The keywords associated with this study include social media, mental health, depression, network behaviour, and tweets.