Emerging Trends in Social Networking Systems and Generation Gap with Neutrosophic Crisp Soft Mapping

This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse NCS-mapping between two NCS-classes.We develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various generations.We investigate the advantages,disadvantages,and natural aspects of SNSs for five generations.With the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by day.The suggested modeling with NCS-mapping is applicable in solving various decision-making problems.

Effect of Online Social Networking on Emotional Status and Its Interaction with Offline Reality during the Early Stage of the COVID-19 Pandemic in China

Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.

Using IQA to Extract Mental Models Concerning Quality Evaluation Factors for Social Networking Services on Smartphones

Although today’s users of mobile phones are afforded increasing control as the popularity and richness of information provided by smartphones increases, how these users make choices and evaluations cannot be explicitly expressed. This study uses an acute observation of designers of technology and aesthetics to conduct a mental-model analysis of smartphones with the purpose of explicitly identifying the differences in the schemas of users. This study also modifies the mental model to evaluate the practicability of the mental model. Hence, this study investigates groups of subjects, comparing freshmen and graduating students froma college of design, and adopts a schematized data collection and analysis approach, Interactive Qualitative Analysis (IQA), to ascertain the relationships between factors and establish a complete user mental model. The study results present 11 primary affinity factors: “Pricing,” “Advertisement,” “User Interface,” “Innovation Functions,” “System Maintenance,” “Privacy,” “Utility Functions,” “Personalization,” “Network Performance,” “Customer Service,” and “Multimedia Contents.” The primary driver in evaluating smartphones is found to be “Pricing” for graduating students and “Privacy” for freshmen. The two groups share the same final effect factor, “Multimedia Contents.” This study has successfully identified differences between the different mental models of the two groups, supporting the method that using IQA to perform a quality evaluation of social networking for smartphones is effective. Consequently, platform developers can understand user demand through a mental model and can design good platform functions to effectively improve users’ experience of smartphones.

Factors influencing knowledge contribution:An empirical investigation of social networking website users

Purpose: In the Web 2.0 era,leveraging the collective power of user knowledge contributions has become an important part of the study of collective intelligence. This research aims to investigate the factors which influence knowledge contribution behavior of social networking sites(SNS) users.Design/methodology/approach: The data were obtained from an online survey of 251 social networking sites users. Structural equation modeling analysis was used to validate the proposed model.Findings: Our survey shows that the individuals' motivation for knowledge contribution,their capability of contributing knowledge,interpersonal trust and their own habits positively influence their knowledge contribution behavior,but reward does not significantly influence knowledge contribution in the online virtual community.Research limitations: Respondents of our online survey are mainly undergraduate and graduate students. A limited sample group cannot represent all of the population. A larger survey involving more SNS users may be useful.Practical implications: The results have provided some theoretical basis for promoting knowledge contribution and user viscosity.Originality/value: Few studies have investigated the impact of social influence and user habits on knowledge contribution behavior of SNS users. This study can make a theoretical contribution by examining how the social influence processes and habits affect one's knowledge contribution behavior using online communities.

Privacy disclosure in Chinese social networking sites:An investigation of Renren users

Purpose:This study was conducted to investigate the current situation of privacy disclosure(in the Chinese social networking sites.Design/methodology/approach:Data analysis was based on profiles of 240 college students on Renren.com,a popular college-oriented social networking site in China.Users’ privacy disclosure behaviors were studied and gender difference was analyzed particularly.Correlation analysis was conducted to examine the relationships among evaluation indicators involving user name,image,page visibility,message board visibility,completeness of education information and provision of personal information.Findings:A large amount of personal information was disclosed via social networking sites in China.Greater percentage of male users than female users disclosed their personal information.Furthermore,significantly positive relationships were found among page visibility,message board visibility,completeness of education information and provision of personal information.Research limitations:Subjects were collected from only one social networking website.Meanwhile,our survey involves subjective judgments of user name reliability,category of profile images and completeness of information.Practical implications:This study will be of benefit for college administrators,teachers and librarians to design courses for college students on how to use social networking sites safely.Originality /value:This empirical study is one of the first studies to reveal the current situation of privacy disclosure in the Chinese social networking sites and will help the research community gain a deeper understanding of privacy disclosure in the Chinese social networking sites.

Should Apple Charge Fees for Tipping on Social Networking Apps?

Recently, Apple＇s decision to start taking a 30-percent cut on tips given by users of socia networking apps has sparked heated debates in China.

WiFace:A Secure GeoSocial Networking System Using Wi-Fi Based Multihop MANET

A number of mobile Online Social Networking （OSN） services have appeared in the market in recent times. While most mobile systems benefit greatly from cloud services, centralized servers and communications infrastructure is not always available. Nor are location-based services offered to mobile devices without GPS. To take advantage of cloud and to address these problems, a Wi-Fi based multihop networking system called MoNet is proposed. On top of MONET we propose a privacy-aware geosocial networking service called WiFace. Where there is no infrastructure, a distributed content sharing protocol significantly shortens the relay path, reduces conflicts, and improves data availability. Furthermore, a security mechanism is developed to protect privacy. Comprehensive experiments performed on MoNet show that the system is more than sufficient to support social networking and even audio and video applications.

Exploring the Reasons for Selfie-Taking and Selfie-Posting on Social Media with Its Effect on Psychological and Social Lives:A Study among Indian Youths

‘Selfie’taking was introduced to the common people by smartphones and has become a common practice across the globe in no time.With technological advancement and the popularity of smartphones,selfie-taking has grown rapidly within a short time.In light of the new trend set by the generation,this study aimed to explore reasons for selfie-taking and selfie-posting on social media and their effects on the social and psychological lives of young adults.A purposive sampling method was adopted to select 20 Indian citizens,between 18 and 24 years.The data were collected through semi-structured interviews and analysed using thematic analysis.Selfie-taking and posting on social media give positive feelings,and it acts as a mood modifier dependent mostly on the favourability and feedback about the post which in turn affects emotions and self-satisfaction.

Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

Discrete Choice Analysis of Temporal Factors on Social Network Growth

Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.

Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.

The Promise and Dilemma of the Mediated Parent-Teacher Relationship in the Age of Social Networking Sites

Based on an ethnographic study on two cohorts of teachers and parents in China,this article reveals that social networking sites(SNS)have widened the channel for parents and teachers to establish and maintain a relationship,and has formed an online“community of practice”to promote such collaborations.Yet,this could be accomplished only at the expense of teachers’professional and personal boundaries becoming increasingly blurred,which has emerged as a potential risk for their professionalism.Moreover,such a“community of practice”has also opened up a new space for winning or losing at the educational game for parents from different background,which has inadvertently led to widening the arena for the operation of old mechanisms of social inequality.This study suggests that further investigation should be conducted to examine the potential of SNS in the parent−teacher relationship.

Software,Social Networking,Interactive:New Trends in Multinational Investment

As far as international venture capital is concerned, three sectors that deserve big injections of cash this year are energy, media and the Internet.

Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.