Behavior In Social Networks Research Articles

Some aspects of teaching schoolchildren in grades 7–9 safe behavior In social networks

Some aspects of teaching schoolchildren in grades 7–9 safe behavior In social networks

Auditory pallial regulation of the social behavior network

Sensory cues such as vocalizations contain important social information. Processing social features of vocalizations (e.g., vocalizer identity, emotional state) necessitates unpacking the complex sound streams in song or speech; this depends on circuits in pallial cortex. But whether and how this information is then transferred to limbic and hypothalamic regions remains a mystery. Here, using gregarious, vocal songbirds (female Zebra finches), we identify a prominent influence of the auditory pallium on one specific node of the Social Behavior Network, the lateral ventromedial nucleus of the hypothalamus (VMHl). Electrophysiological recordings revealed that social and non-social auditory stimuli elicited stimulus-specific spike trains that permitted stimulus differentiation in a large majority of VMHl single units, while transient disruption of auditory pallium elevated immediate early gene activity in VMHl. Descending functional connections such as these may be critical for the range of vertebrate species that rely on nuanced communication signals to guide social decision-making.

Scholarly Communication and Information Behavior in Chinese Social Networks: A Sentiment Analysis of WeChat Academic Communities

WeChat has penetrated into academic communication circles; however, there is still a significant gap in understanding the way sentiment and information behaviors shape scholarly discourse on this platform. The present research aimedto explore the scholarly communication and information behavior in the Chinese social networks. For this purpose, a sentiment analysis of WeChat academic communities was performed. This study adopted a comprehensive methodology that involved collection of 800 WeChat articles on the basis of engagement metrics, followed by the data pre-processing. It involved cleaning, Chinese words segmentation using Jieba library and TF-IDF vectorization for text analysis. Results of SVM model demonstrated robust performance in sentiment analysis with an overall accuracy of 89% and consistent precision and recall rates across the sentiment categories. Comparison with existing studies also highlighted effectiveness of this model in classifying sentiments on WeChat. Utilization of SVM in sentiment analysis advances the theoretical understanding of text classification techniques in social media environments. The findings also provide valuable insights for researchers and practitioners so that they can leverage SVM for effective classification of SVM. Limitations and future research indications have also been explained in the study

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

Feasibility and Acceptability of a Prospective Syphilis Sexual Network Study for Sexual Minority Men.

In preparation for a prospective syphilis network study of sexual minority men, we conducted a mixed-methods formative study with the following objectives: a) assess acceptability of respondent driven sampling (RDS), b) assess acceptability of study procedures, c) social network seed selection, and d) pilot an ecological momentary assessment (EMA) study to assess social networking, sexual and substance use behavior. We conducted in-depth interviews with eight providers serving sexual minority men and five focus group discussions with 34 sexual minority men, prioritizing four target populations: young Black sexual minority men, 2) on PrEP, 3) living with HIV, 4) not engaged in care. The 4-week EMA pilot was conducted with 40 sexual minority men. EMA survey responses were analyzed to evaluate how different compensation levels influenced response rates. Brief exit surveys were used to assess EMA app acceptability. Primary themes identified through qualitative data collection: 1) importance of developing trust and maintaining confidentiality during proposed recruitment activities, 2) importance of compensating participants appropriately for study activities, and 3) cultural considerations for increasing visibility and participation of young Black sexual minority men. All EMA participants reported being "completely comfortable" reporting sexual behavior through the app. Most (78%) preferred the app to in-person interviews. Several participants identified technical issues with the app, including not receiving push notifications and spontaneous closure. This mixed-methods formative study allowed for adjustments to and tailoring of the planned network study, including recruitment protocols, compensation type and amount, and EMA survey wording and response items.

PHYSICAL ANALYSIS OF SOCIAL DYNAMICS: A SOCIOPHYSICS PERSPECTIVE

Sociophysics is an interdisciplinary field that uses methods from the physical sciences to study human behavior and interactions. It includes mathematical and computational techniques such as big data analysis, statistical modeling, network theory, and simulations. It analyzes complex systems to understand the dynamics of society. Historically, sociophysics emerged from applying statistical mechanics and thermodynamics to social phenomena. Foundational work has modeled opinion dynamics, crowd behavior, and information diffusion in social networks, and provided insights into consensus, polarization, and social stability. The basic concepts are based on treating individuals as agents, using network theory, and applying statistical mechanics and dynamical systems. Methods include big data analysis, statistical modeling, simulations, and network analysis. Future research will aim to integrate artificial intelligence and machine learning, foster interdisciplinary collaboration, utilize real-time data, and apply findings to public policy. Sociophysics aims to improve our understanding of social systems and solve today's society's complex problems. In this study, the field of sociophysics, its historical development, studies in the literature, and methods were generally discussed.

The function of OforOrco in the allogrooming behavior of Odontotermes formosanus (Shiraki) induced by Serratia marcescens Bizio (SM1)

Termites are consistently confronted with a complex microbial environment. In addition to the role of their innate immune system in resisting pathogen infection, social immune behavior also plays a significant role in helping termites withstand the stress caused by pathogenic microorganisms. The allogrooming behavior among different individuals is commonly observed in termites, and it plays a crucial role in the social immune interaction network. In the case of Odontotermes formosanus (Shiraki), Orco is specifically involved in detecting pheromones and volatile chemicals released by termites to communicate with each other. Nonetheless, the function of Orco in the social immunity remains unreported in O. formosanus. Consequently, in this study, we recorded the allogrooming behavior of O. formosanus workers under SM1 stress. The results indicated a significant increase in allogrooming behavior due to SM1 infection. The allogrooming behavior of workers under SM1 stress was significantly increased after the addition of soldiers. Compared with pronotum group treated by SM1, SM1 treatment of workers' heads significantly reduced the allogrooming behavior among workers. In addition, we found that SM1 could greatly increase the expression of OforOrco. Furthermore, interfering with OforOrco could markedly reduce the allogrooming behavior among workers under SM1 stress, and increase the mortality of worker under SM1 stress. This study demonstrated the significant role of OforOrco in the social immunity of O. formosanus, which offers a theoretical foundation for the advancement of research on termite RNA biopesticides, and the integration of RNA interference (RNAi) with pathogens. This study is valuable for elucidating the social immune behavior and interaction network of termites.

Social Networks Shape Beliefs and Behavior: Evidence from Social Distancing during the COVID-19 Pandemic

Social Networks Shape Beliefs and Behavior: Evidence from Social Distancing during the COVID-19 Pandemic

Brain Gene Regulatory Networks Coordinate Nest Construction in Birds.

Nest building is a vital behavior exhibited during breeding in birds, and is possibly induced by environmental and social cues. Although such behavioral plasticity has been hypothesized to be controlled by adult neuronal plasticity, empirical evidence, especially at the neurogenomic level, remains limited. Here, we aim to uncover the gene regulatory networks that govern avian nest construction and examine whether they are associated with circuit rewiring. We designed an experiment to dissect this complex behavior into components in response to pair bonding and nest material acquisition by manipulating the presence of mates and nest materials in 30 pairs of zebra finches. Whole-transcriptome analysis of 300 samples from five brain regions linked to avian nesting behaviors revealed nesting-associated gene expression enriched with neural rewiring functions, including neurogenesis and neuron projection. The enriched expression was observed in the motor/sensorimotor and social behavior networks of female finches, and in the dopaminergic reward system of males. Female birds exhibited predominant neurotranscriptomic changes to initiate the nesting stage, while males showed major changes after entering this stage, underscoring sex-specific roles in nesting behavior. Notably, major neurotranscriptomic changes occurred during pair bonding, with minor changes during nest material acquisition, emphasizing social interactions in nest construction. We also revealed gene expression associated with reproductive behaviors and tactile sensing for nesting behavior. This study presents novel neurogenomic evidence supporting the hypothesis of adult neural plasticity underlying avian nest-construction behavior. By uncovering the genetic toolkits involved, we offer novel insights into the evolution of animals' innate ability to construct nests.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Objective. Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors. Approach: We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Main results: Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth. Significance: Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

Validation of the brief screening of Social Network Addiction Risk

IntroductionThis study aims to develop a valid and reliable tool to evaluate social network behavior in young adults. MethodsTo validate the Brief screening for Social Network Addiction Risk (BSNA), data from 776 Italian young adults (64.3 % of women) were collected. The suitability of the instrument was statistically assessed. Experts' opinions, item reliability, exploratory and confirmatory factor analyses, and convergent validity were adopted to validate the BSNA items. Internal consistency coefficients were also calculated. ResultsAccording to the statistical analyses, a 2-factor structure was confirmed. The two scales of BSNA assess behavior and motivation frame of social networks use. The second order model proved a global score of risk of social network addiction. Fit indices highlighted the high goodness of the model. Preliminary analyses of prevalence estimated that about 18 % of participants reported problematic Social Network use, which may overtime represent a marker of addictive behavior. ConclusionThe final version of the BSNA, with 11 items evaluated on a 5-point Likert- scale, is a short but valid tool for measuring the risk of social network addiction. It represents a promising screening tool aimed to not overpathologize a behavior, but to furnish adequate insight into this phenomenon.

Bibliometric Analysis of Scientific Publications on Communicative Behavior Problems

Purpose of research. The purpose of the study is to analyze the dynamics of the publication activity of scientific research in the field of studying communicative behavior. Communicative behavior is an object with an interdisciplinary research status. It falls into the area of research interest of various scientific fields. A bibliometric analysis of publication activity was carried out in connection with solving several tasks: to find out areas of knowledge in which the greatest publication activity is concentrated, to study the methods that are used in various fields of communicative behavior analysis, to identify organizations that are included in this research search.Methods. The study was conducted in two scientific electronic databases: the Russian Scientific Electronic Library Elibrary.ru and the international open platform for researchers and scientific organizations Lens. The bibliometric analysis was used as the research method. It involves the quantification of documentary information flows and it is based on quantitative research indicators reflecting the state of science as a whole or its individual branches.Results. As a result of the bibliometric analysis of publication activity on the problems of scientific research in the field of communicative behavior, communicative behavior in the digital environment and communicative behavior in social networks, a significant increase in publication activity was revealed in both international and Russian information databases over the past 10 years (2013 - 2023). It is also noted that the predominant number of publications on the issues studied is concentrated in the field of psychology. However, we can trace a shift in the focus of foreign research on communicative behavior towards digitalization in information technology, and Russian research towards the field of digital technologies in the educational environment.Conclusion. The results obtained indicate the high relevance, as well as the scientific and practical significance of the research topic on the problems of communicative behavior in general, as well as communicative behavior in the digital environment and social networks, in particular.

Social Support Network and Sedentary Behavior Among US Adults With and Without Mobility Impairment.

Evidence suggests that sedentary behavior is increased among those with mobility impairment, but the evidence examining the impact of social support networks (SSN) on this relationship remains limited. This study examines the relationship between SSN and sedentary behavior among adults with and without mobility impairment. Cross-sectional. National Health and Nutrition Examination Survey (NHANES) 2007-2008. U.S. adults (≥40years of age) with (n = 962) and without (n = 1393) mobility impairment. The Social Support Network (SSN) score was created using self-reported emotional, financial, church, friends, and marital support/status. The dependent variable in this study was the upper quartile of sedentary time in minutes. Both higher SSN score (AOR .43; 95% CI 0.25-.76, P = .045) and poverty to income ratio (PIR) (AOR 2.60; 95% CI 1.40-4.82, P = .01) were significant predictors of increased sedentary time among adults with mobility impairment. Among adults without mobility impairment, higher PIR was the only significant predictor of increased sedentary time (OR 3.59; 95% Cl 1.80-7.15, P < .0001). Higher SSN score was associated with significantly lower odds of increased sedentary time among adults with mobility impairment. A similar relationship was not revealed among adults without mobility impairment. Higher PIR was associated with significantly higher odds of increased sedentary time among adults with and without mobility impairment.

The influence of business students’ horizontal and vertical network behavior on their learning and performance

Drawing on network behavior literature, this research examines how business students’ extra-classroom social network behavior for learning with peers (i.e., horizontal network behavior) and with the instructor (i.e., vertical network behavior) influences their learning, which in turn affects their grade performance. It also investigates the moderation of perceived instructor support on the relationship between their vertical network behavior and learning. Using data from 163 undergraduate business students, we found that extra-classroom horizontal and vertical network behavior for learning increases their grade performance via enhanced learning. We also found that extra-classroom vertical network behavior is more likely to be positively associated with their learning when they perceive strong instructor support.

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.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks

Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks

Impact of Psoriasis on Patients' Social Network Behavior.

Impact of Psoriasis on Patients' Social Network Behavior.

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.