Social Network Users Research Articles

Conditional heavy hitter monitoring and application of heterogeneous graph streams based on sketches

Graph streams offer valuable insights into real network evolution in the era of big data. Heavy hitters and conditional heavy hitters focus on identifying entities with significant network traffic, revealing network data characteristics. In this paper, we present two methods, BLSketch and MHLSketch, for identifying heavy hitters and conditional heavy hitters in heterogeneous graph streams. BLSketch uses the bidirectional mapper, Bimap, for efficient query response times while retaining candidate information. MHLSketch uses min heap to preserve a minimal set of candidates to minimize storage space, excelling in space efficiency. The two methods can be incorporated with any sketch based graph summarization method, showing excellent adaptability. Experimental results on four real-world heterogeneous datasets of varying scales with the largest dataset containing millions of edges demonstrate our methods significantly improve the quality of query answering, outperforming state-of-the-art methods, with precision close to 1 and average relative error close to 0. We further combine our methods with density clustering to introduce the SDC model, which efficiently identifies hot regions and key users in Location-Based Social Networks (LBSN). In experiments, SDC outperforms traditional clustering by reducing up to 93.1% of cold Points of Interest (POIs) and shows strong potential for practical applications.

Demystifying the Effect of Social Media Advertising on Purchase Intention of Millennials: Role of eWOM and Privacy Concerns

The substantial impact of businesses’ strong online presence on consumers’ spending habits, especially among decision-makers, cannot be overstated. One of the forms of online product reviews is electronic word-of-mouth (eWOM), and it has a significant impact on consumers’ attitude towards a product as well as their intentions to purchase. This article aims to provide a conceptual framework that assesses how eWOM and privacy issues affect Indian millennials’ attitude towards social media advertising and the subsequent effect of attitude on purchase intention. The data were gathered through non-probability sampling from 340 social network users utilising an online questionnaire through Google Forms. The adapted scales were validated using confirmatory factor analysis, followed by path analysis using SPSS AMOS 26.0 to examine numerous hypotheses that had been formulated. The results revealed significant relationships that are helpful in recognising the attitude and purchase intention of Indian millennials regarding social media advertisements. The study can be helpful to brand managers, marketing executives, and advertisers in generating social media commercials by incorporating key elements that can positively mould attitudes and further develop purchase intention.

Influence of Social Networking Usage on Mental Health of Tribal Adolescents

Abstract From past two decades , Social Networking Sites beheld a sporadic enhancement in quantity, quality and utility. As the body of an individual is nourished by the intake of necessary mineral elements obtained through nutrition, likewise human mind is nurtured by the availability of nutrition for thoughts. Social networking sites have become an important part of modern life. They are generally defined as web-based platforms that allow individuals to create their own personal profile and build a network of connections with other users. From the dawn of internet and social networking sites, human resources of contemporary world have become more social virtually but less practically. The virtual life is isolating present generation from their other fellow beings there by affecting their health both physically as well as mentally and overall balance. Increased usage of social networking sites particularly among adolescents in the present scenario is a matter of concern for the parents, society and researchers as there are always two sides (positive and negative) of every innovation. The aim of the present research study was to explore the influence of social networking sites on mental health of the adolescents of three government secondary school of Koraput district, Odisha. For the present study, an ex-post facto research method was employed. The stratified random sampling technology was employed to select the sample where total of 150 adolescent boys and girls from class -X ( 50 from each school) were purposively selected. The data was collected by administering a standardized tool based on mental health scale upon the students and the collected data was analysed by using several statistical techniques like- Mean, SD and t-test. This, the findings showed that there was significant difference between the mental health conditions of social networking users and non-users. The findings also showed that there was also significant difference between social networking users' mental health in terms of gender and locality.

Effect of Positive Psychology Online Group Therapy on Spiritual Well-Being, Positive and Negative Affect of Working Women in COVID-19 Pandemic

Background: Positive psychology is a branch of psychology that deals with the factors shaping mental health, even in dangerous situations such as the COVID-19 pandemic, by emphasizing human capabilities. Objectives: The present study aimed to investigate the impact of positive psychology in online group meetings on the spiritual well-being and positive and negative effects of Iranian working women during the COVID-19 pandemic. Methods: The present study was a clinical trial conducted with a pre-test, post-test, 1-month follow-up, and a control group conducted on all Iranian working women aged 18 - 45 years who were users of social networks (such as Instagram, Telegram, and WhatsApp) in 2022 - 2023. The sample was selected by convenience sampling and included 52 women assigned to the experimental (26 women) and control (26 women) groups. Data were collected using the Spiritual Well-Being scale (SWBS) of Paloutzian and Ellison and the Positive and Negative Affect Schedule (PANAS). The experimental group received 6 one-hour training sessions. The data were analyzed using a multivariate analysis of covariance (MANCOVA) through SPSS software (version 22). Results: The MANCOVA results revealed that after the intervention, there was a significant difference between the groups regarding the positive affect variable (P < 0.001), while no difference was observed between the two groups in spiritual well-being and negative affect (P > 0.05). Conclusions: The findings recommend interventions based on positive psychology to increase positive affect due to its emphasis on the positive points of the current situation and strengthening them, leading to the adoption of an active stance towards life. On the other hand, the insignificant effect of the treatment on spiritual well-being and negative effects necessitates more studies and more serious and long-term interventions.

È un complimento?, Ist das ein Kompliment für dich?. Implicit Compliments in Italian and German Social Networks

Compliment is one of the most widely studied speech acts, as the extensive literature devoted to this topic shows. However, researches mainly focus on formulaic complimenting, probably because explicit compliments are far more frequent than the implicit ones. The present contribution aims to investigate implicit compliments, i.e., those where hearers «need to infer the corresponding implicature for their interpretation» (MAÍZ-ARÉVALO, 2012, p. 983; cf. also BOYLE, 2000), in a corpus of digital exchanges in Italian and German social networking sites, Instagram and WhatsApp. Firstly, the study examines and compares, from both a qualitative and quantitative point of view, the linguistic patterns used to express implicit compliments in the two languages; then, through a detailed analysis of the reactions of compliments’ addressees it explores how these speech acts are perceived by Italian and German social network users and what response types are mainly used by the complimented person in order to react to the implicit positive evaluation.

Networks and Influencers in Online Propaganda Events: A Comparative Study of Three Cases in India

The structure and mechanics of organized outreach around certain issues, such as in propaganda networks, is constantly evolving on social media. We collect tweets on two propaganda events and one non-propaganda event with varying degrees of organized messaging. We then perform a comparative analysis of the user and network characteristics of social media networks around these events and find clearly distinguishable traits across events. We find that influential entities like prominent politicians, digital influencers, and mainstream media prefer to engage more with social media events with lesser degree of propaganda while avoiding events with high degree of propaganda, which are mostly sustained by lesser known but dedicated micro-influencers. We also find that network communities of events with high degree of propaganda are significantly centralized with respect to the influence exercised by their leaders. The methods and findings of this study can pave the way for modeling and early detection of other propaganda events, using their user and community characteristics.

Fuzzy Influence Maximization in Social Networks

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user’s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user’s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.

An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Recently, social network usage has exhibited explosive growth, leading to a huge amount of users’ private data available. The main challenge in releasing social network data publicly is the protection of the users’ privacy while preserving its utility for third parties. Accordingly, several social network privacy-preserving methods have been introduced, where anonymization is the most common approach. Structural k-anonymity is a widely used anonymization model to mask the structure of social networks by clustering the edges and nodes into super-edges and super-nodes. However, it comes at the cost of losing structural information, which is measured by a criterion called structural information loss (SIL). This study introduces an enhanced discrete particle swarm optimization (EDPSO) algorithm, which effectively minimizes the SIL within the clustering process of the structural k-anonymity model, leading to a high-utility anonymized network. In this regard, we propose a vector-based solution representation that can be efficiently exploited by the EDPSO. Moreover, a novel position updating heuristic is suggested for the EDPSO, which adaptively tunes the operators’ selection probabilities. This happens based on each operator’s performance both in the current iteration and their history regarding the number and the average amount of fitness improvements in the previous iterations. We also propose two fortified versions of the EDPSO algorithm (EDPSOVNS and EDPSOSA) by employing two new network-specific local search strategies to enhance the exploration, exploitation, and convergence rate of the process. Simulation results on the nine real-world networks demonstrate the superiority of the suggested algorithms in terms of the fitness value, reliability, and convergence rate over other analyzed approaches found in the literature.

Fulfilling the promise of digital tools to build rangeland resilience

The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise.

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.

Reconstructive Classification for Age and Gender Identification in Social Networks

Author profiling is a challenging task that consists of identifying a person’s relevant attributes based on the content he/she generates. In this article, we validate a classification method based on the reconstructive classification to identify two demographic attributes, age and gender, for users of social networks based on the text content they publish. For the problem, we consider balanced and unbalanced data, where a gender or age group has a larger presence than the others. The proposed method is based on using the reconstructive property of singular value decomposition (SVD), and its suitability to work with sparse data, to find a matrix with the main latent components to represent the information of specific gender or age classes. Afterward, we use such a matrix to project and reconstruct new users’ information to identify their demographic variables. We test our method with a set of datasets in several languages collected from Twitter and Pinterest users, and we use different evaluation metrics to compare their performance with the ones of several popular classifiers based on traditional machine learning, and on deep learning, and with some relevant works in the literature. The results show that the proposed classifier performs generally well in identifying the age and gender of users in social networks.

"Russian-Ukrainian conflict" through the prism of users of the Chinese social network "Weibo"

The subject of this article is the views of Chinese netizens on the "Russian-Ukrainian conflict". The object of the study is posts about this topic on the Chinese social network "Weibo". The authors analyze the importance, functions of the Weibo in the production of public opinion and its main opinion leaders. Then the article examines the number of posts in month (15.02-15.03) compared to other international events and makes a content analysis of posts to find out the main topics and the attitude of users on the social network "Weibo" to the "Russian-Ukrainian conflict". At the end of the article, authors learn the relationship of the research results with stereotypes about the image of Russia in China. The results of the study show that Chinese Weibo users began to care about the event from the beginning of a special military operation, but interest faded with the first round of negotiations between Russia and Ukraine. Chinese Weibo users pay attention to the role of the United States in the conflict and Western sanctions against Russia. They mainly reflect support for the Russian economy and opposition to Western sanctions, which also affects the Chinese government's statement on this event. The article considers that such views are closely connected with stereotypes about the image of Russia in China. The results of the study show that in future international events, the peoples of Russia and China have more opportunities to reach agreement.

Impact of exposure to Covid-19 related News on the sensation of fear among Brazilians

Seventeen months after the decree of the COVID-19 pandemic and in a scenario of greater certainty and knowledge about the disease and its behavior, a survey was carried out with the objective of investigating the Impact of exposure to Covid-19 related News on the sensation of fear among Brazilians. For that, a self-administered electronic questionnaire was applied to 246 social network users selected through the snowball method. Data interpretation was performed using descriptive statistics and the use of a correlation matrix. The results pointed to a significant relationship between nervousness and anxiety when hearing news about COVID-19 and the physical and psychological sensations of fear Fear of COVID-19, Discomfort when thinking about COVID-19 and Fear of die from COVID-19 is in line with other research. Contrary to the academic literature, a significant reference to Sleep trouble from worry about contracting COVID-19 was not found in this study. In this study, questions were not asked about the influence of different means of communication in this process or the role of moderators such as gender and age, gender or age, being this information (at the same time) limitations of this work and suggestions for future studies . Because it was conducted seventeen months after the pandemic was declared, the results reflect the consequences of prolonged and frequent exposure to the subject.

The <i>fake news</i> during the chilean social outbreak and the role of <i>fact checking</i> against disinformation

The objective of this research is to understand the impact generated by fake news on the political perception and critical thinking of citizens who receive information through social networks during the Social Outbreak in Chile, and to assess the value of new independent fact checking media created during the same period. The methodology employs a mixed approach with an exploratory scope, involving the creation of an assessment tool used to evaluate 95 subjects from the Biobío Region, Chile who assessed and categorized a news item as true or false. The primary findings reveal that participants perceive sharing fake news as a perilous action, and they hold the belief that fake news has the potential to alter political and social ideologies. In conclusion, assessing subjects' strategies for identifying fake news allows for the development of evaluation tools that promote data verification and enhance media literacy. This becomes crucial as the criteria for verification among active social network users are often low, indicating a level of credibility in published content without thorough scrutiny of data sources or acknowledgment of the risks associated with disseminating potentially false information.

Model development of the system for avoiding echo chambers in social networks

This article examines the problems that appear in the process of the development of social networks. The focus of the study is on the echo chamber effect in various social networks. It turned out that the echo chamber effect is the result of the work of a deep neural network, which analyzes the interests and priorities of each social network user, checks other similar posts and priorities of other users, and then forms a “circle” of like-minded people for each user. Thus, the main drawback of the standard content generation algorithm is the selection of only publications and comments that support the user’s position. The goal of the study is a software architecture to solve the echo chamber problem in highly loaded social networks in real-time. The main idea was to cluster opinions on controversial topics and include user opinions from different clusters in user content. Data on controversial topics is collected from news, scholarly articles, publications, and comments with hashtags. The collected topics are clustered usi ng the K-means algorithm and the “elbow” method is used to find the optimal number of clusters. The result of the clustering of opinions by topic is provided as input data for the generation of a news feed by a recurrent neural network. The system uses Kafka as a message broker between microservices and AZURE blob storage for storing publications and comments. Both solutions support high scalability.

Evaluating the Impact of Social Networks on Human Communication in the Digital Era

The widespread adoption of social networks has significantly transformed human communication and interaction patterns in contemporary society. The purpose of the study is to examine how individuals perceive and experience social network usage, aiming to shed light on its implications for interaction frequency, communication styles, social unity, collective action participation, and overall well-being. Employing an observational and descriptive research design, data was collected from a diverse sample of 120 participants using surveys administered online or in-person. Convenience sampling techniques ensured a balanced representation across age groups, genders, occupations, and geographical locations. Data analysis involved a combination of quantitative and qualitative approaches, with descriptive statistics summarizing demographic characteristics and survey responses. Thematic analysis was employed to identify recurring themes and patterns in qualitative data. Results reveal diverse perceptions regarding social network impact, with Facebook emerging as the most preferred platform for human communication. Participants reported varying levels of interaction frequency, communication styles, and concerns about negative effects on mental health. In conclusion, the study underscores the nuanced nature of social network dynamics and their implications for human communication and well-being. Recommendations include promoting digital literacy and mindfulness to mitigate potential negative impacts while harnessing the positive aspects of social network usage for enhancing social connections and community engagement.

Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.

Professionals' digital competences and user profiles in social agencies and their impact on professional practice, family autonomy and wellbeing.

The health crisis of the last 3 years has revealed the weaknesses of the child and family support system based on the professional use of digital resources in social agencies. This study addresses three aims: to examine the level of professional digital competence; to analyze the user profiles in a variety of digital resources; and to test the impact of level of competences and user profiles on four aspects: professional practice, family satisfaction with the services, child and family wellbeing, and family autonomy in the exercise of the parenting role. Participants were 148 practitioners from social agencies who voluntarily responded to an online survey with 47 questions. Results showed that professionals perceive themselves as more competent in areas of information / data management and communication / collaboration than in the creation of digital content, security measures, and technical problem solving. Websites, email, and instant messaging were the sources more frequently used and with higher satisfaction, than structured programs, social networks and multimedia content. Variability in the user profiles showed three clusters: Cluster 1 Social network user (n = 13), Cluster 2 Diversified user (n = 75) and Cluster 3 Communicative instant user (n = 60). Participants in Cluster 2 compared to those in the other clusters were the most proficient on their digital competences and acknowledge the positive impact of digital resources on their professional practice and the psychological and social wellbeing of families. This study points the need for improvement in professionals' digital competences in some of the measured areas and the user profile of digital resources since both provide benefits on professional practice and family autonomy and wellbeing.

Uses and gratifications of social networking use: Associations with social capital and subjective well-being

BACKGROUND: In recent years, social media has surged in popularity and affected every facet of people’s lives from the office environment to leisure time by transforming their lifestyles and influencing well-being. OBJECTIVE: The purpose of this study is to investigate the impact of social networking usage on the subjective well-being of users by first determining the key gratifications that motivate them to use social networking sites by applying the uses and gratification theory, and then focusing on psychological outcomes such as social capital that affect their subjective well-being. METHOD: The data were collected from 400 students of reputed universities in North India by adopting a judgemental sampling technique. RESULTS: Results indicated that key gratifications such as entertainment, tension release, personal integration, information seeking, emotional connection and socialization played a significant role in social networking usage. Moreover, social networking usage had a significant impact on bonding social capital, bridging social capital and subjective wellbeing. Furthermore, results showed that bonding social capital, and bridging social capital could significantly mediate the relationship between social networking usage and subjective well-being. CONCLUSIONS: Social networking usage has the potential to promote social capital and subjective well-being by addressing the fundamental needs of users in life.

How social media sharing drives consumption intention: the role of social media envy and social comparison orientation

BackgroundSocial media benign envy, an upward comparison-based and painful emotions associated with the motivation to improve oneself, has attracted increasing attention from researchers due to its ubiquitous and significant impact on social network users’ intentions and behavior. However, the results of previous studies on whether material or experiential consumption is more likely to cause social media envy (treated as a single construct) have been inconsistent, and there is a lack of research on what triggers social media users to experience more intense benign envy and thus inspiring their consumption intentions. The purpose of this study is to investigate how the type and luxuriousness of shared consumption and viewer’s social comparison orientation jointly affect social media users’ consumption intentions through benign envy.MethodsA 2 (type of consumption sharing: experiential vs. material) × 2 (luxuriousness of consumption sharing: luxury vs. non-luxury) × 2 (social comparison orientation: high vs. low) mixed-design experiment was conducted to test theoretical model with data from 544 undergraduates in China. SPSS 26.0 and the Process macro were used to test the model.ResultsThe results revealed that luxury experiential consumption information shared on social media triggered more benign envy compared with other types of shared consumption information. When social media users shared non-luxury consumption, experiential consumption was more likely to inspire benign envy among users with high social comparison orientation than material consumption. However, when luxury consumption was shared, benign envy acted as a mediator between purchase type and participants’ purchase intention regardless of whether participants’ social comparison orientation was high or low.ConclusionThis study revealed that whether and how social comparison orientation of social media users who read the shared content influences the mechanism by which the type of consumption sharing on social media affects social media users’ consumption intentions through benign envy as a mediator is dependent on the luxuriousness of the shared consumption. The findings not only provide new insights for researchers to better understand social media envy and the underlying psychological mechanism for social media readers’ consumption intention, but also have practical implications for practitioners.