Behavior In Social Networks Research Articles

Spatiotemporal Persistent Homology for Dynamic Metric Spaces

Characterizing the dynamics of time-evolving data within the framework of topological data analysis (TDA) has been attracting increasingly more attention. Popular instances of time-evolving data include flocking/swarming behaviors in animals and social networks in the human sphere. A natural mathematical model for such collective behaviors is a dynamic point cloud, or more generally a dynamic metric space (DMS). In this paper we extend the Rips filtration stability result for (static) metric spaces to the setting of DMSs. We do this by devising a certain three-parameter "spatiotemporal" filtration of a DMS. Applying the homology functor to this filtration gives rise to multidimensional persistence module derived from the DMS. We show that this multidimensional module enjoys stability under a suitable generalization of the Gromov-Hausdorff distance which permits metrizing the collection of all DMSs. On the other hand, it is recognized that, in general, comparing two multidimensional persistence modules leads to intractable computational problems. For the purpose of practical comparison of DMSs, we focus on both the rank invariant or the dimension function of the multidimensional persistence module that is derived from a DMS. We specifically propose to utilize a certain metric d for comparing these invariants: In our work this d is either (1) a certain generalization of the erosion distance by Patel, or (2) a specialized version of the well known interleaving distance. We also study the computational complexity associated to both choices of d.

Selectivity in posting on social networks: the role of privacy concerns, social capital, and technical literacy

People's posting behaviors in social networks was perceived as ambiguous, with concerns misaligned with people's public postings. To address this gap, we suggest a model that offers new insights into the relationship between perceptions and actual behaviors. We define a quantitative marker for agility, the frequency in which people update their audience selection when posting information in online social networks, and evaluate the factors that contribute to the variability of agility between different users. We analyzed the posting behavior of Facebook 181 participants, as well as their answers to open and close questions. We find that frequent changes in privacy settings are correlated with high social privacy and with institutional privacy concerns, whereas social concerns were found to be more prominent. Agility was negatively correlated with low public sharing. Our findings show that users use privacy settings to effectively mitigate privacy concerns and desires for creating and strengthening social connections. We discuss how agility can be used to design and to evaluate new user interfaces for managing privacy in social settings.

Bored and online: Reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood

IntroductionThe current study examined motivations for social networking site use across three years during the transition from late adolescence to emerging adulthood. While research has been conducted examining reasons for social networking site usage and behavior, the clear majority have focused on samples of undergraduate college students and are cross-sectional. MethodsChanges in motivations for using social networking sites were examined in relation to problematic social networking site use and several behavioral and mental health outcomes in a sample of adolescents over three years. ResultsUsing social networking sites to connect with others was relatively stable over a three-year period. However, using social networking sites to seek information increased from late adolescence to emerging adulthood and was not related to any negative outcomes across three years. Using social networking sites to alleviate boredom also increased over time. Initial levels of social media use to alleviate boredom were associated with problematic social networking site use, financial stress, anxiety, and empathy at year three. Increases in using social networking sites to socially connect over time was related to problematic social networking site use, anxiety, delinquency, and empathy at year three. Using social networking sites for any reason was not related to depressive symptoms over three years. ConclusionsThe current study supports the growing body of literature suggesting that using social networking sites to alleviate boredom and socially connect, may place individuals at increased risk for developing pathological tendencies and patterns of behavior towards social networking sites.

Information Propagation Formalized Representation of Micro-blog Network Based on Petri Nets

The description of user behavior in social networks is an important issue for studying social networks. Given that Petri nets can describe the resource flow problem, this study utilizes the features of Petri nets to portray the user behavior states during the message propagation of a micro-blog network and presents an information propagation formalized representation method of a micro-blog network. On this basis, this study analyzed the proposed formalized representation method in detail. We provide examples of applying formalized representation (e.g., micro-blog network addiction of users, user behavior influence, and public opinion analysis). In addition, we introduce the algorithms of formalized representation. We conduct experiments using Sina micro-blog data. Results show that the information propagation formalized representation method of micro-blog network based on Petri nets can depict user behaviors of micro-blog network intuitively and accurately. This study reveals a new perspective for information transmission of a micro-blog network and provides some tools to support public opinion monitoring and micro-blog marketing applications.

Realistic decision-making process with memory and adaptability in evolutionary vaccination game

Abstract Of particular importance for controlling infectious diseases by voluntary vaccination is how to understand individuals’ strategic vaccination behavior in social networks. Previous studies have always assumed an imitating and learning process by the Fermi’s rule, however, individuals’ stochastic behavior of whether to vaccinate is influenced by their intentions instead of simply imitating others. In this paper, a new strategy updating rule is proposed, demonstrating a more realistic vaccination decision-making process with memory effect and adaptability. Individuals determine whether vaccination is significant by comparing the vaccination cost and the average cost of their unvaccinated neighbors, and accordingly change their intentions to vaccinate in the next season, during which the memory effect and adaptability are taken into account. We find that the new updating rule has an extraordinary impact on the vaccination behavior of individuals, and, as a result, on the final epidemic size. It is especially noteworthy that, comparing with the traditional model, the vaccination coverage is improved and the infection size is reduced. Our results highlight that it is necessary to consider memory effect and adaptability in controlling the infectious spreading.

Social Recommendation With Learning Personal and Social Latent Factors

Due to leveraging social relationships between users as well as their past social behavior, social recommendation becomes a core component in recommendation systems. Most existing social recommendation methods only consider direct social relationships among users (e.g., explicit and observed social relations). Recently, researchers proved that indirect social relationships can be effective to improve the recommendation quality when users only have few social connections, because it can identify the user interesting group even though the users have no observed social connection. In the literature, separate two-stage methods are studied, but they cannot explicitly capture the natural relationship between indirect social relations and latent user/item factors. In this paper, the main contribution is to propose a new joint recommendation model taking advantage of the Indirect Social Relations detection and Matrix Factorization collaborative filtering on social network and rating behavior information, which is called as InSRMF. In our work, the user latent factors can simultaneously and seamlessly capture user's personal preferences and social group characteristics. To optimize the InSRMF model, we develop a parallel graph vertex programming algorithm for efficiently handling large scale social recommendation data. Experiments based on four real-world datasets (Ciao, Epinions, Douban and Yelp) are conducted to demonstrate the performance of the proposed model. The experimental results have shown that InSRMF has ability to mine the proper indirect social relations and improve the recommendation performance compared with the testing methods in the literature, especially on the users with few social neighbors, Near-cold-start Users, Pure-cold-start Users and Long-tail Items.

Predicting User Retweeting Behavior in Social Networks With a Novel Ensemble Learning Approach

Information sharing through online social networks has become a main mechanism by which people share information with their friends through retweeting behaviors, which may result in a variety of information diffusion cascades on social media such as Facebook, Twitter, and Weibo. Predicting user retweeting behavior in those social networks is extremely challenging. To complete the prediction task, identifying factors that affect retweeting behavior and constructing efficient model are necessary. In this paper, we study heterogeneous relation networks by considering various social interactions, which reflect how a particular retweeting action is affected by the social behaviors performed by the sender and the receiver of the retweet. We then generate various features from our identified factors belonging to three dimensions - content semantics, user diffusion behavior, and network structure. Moreover, we cast our prediction problem as an ensemble learning problem and propose a novel ensemble learning approach to solve the problem. Combing the generated features and the novel ensemble learning approach, we then propose a model named Retweeting Behavior on Multiple Heterogeneous Diffusion Relation Networks (RBMHDRN) to predict user retweeting behavior in social networks. Experiments on a real dataset extracted from a social network site Weibo demonstrate the effectiveness of our proposed model, indicating that our generated features and proposed approach can significantly improve the performance of predicting user retweeting behavior occurring in the process of information diffusion in social networks.

Experiências na Pós-Graduação, Comportamento nas Redes Sociais e Bem-Estar

Resumo: O objetivo do estudo consiste em analisar a relação entre a satisfação na pós-graduação e o comportamento exteriorizado nas redes sociais, mediado pelo bem-estar subjetivo. O estudo envolve 1.173 alunos de cursos stricto sensu. As evidências encontradas confirmam a relação, em especial quando esta se refere as relações interpessoais. Assim, a satisfação com a pós-graduação ultrapassa os limites da formação profissional e os laços sociais estabelecidos interferem no bem-estar dos alunos, o qual é exteriorizado nas redes sociais. O ambiente de compartilhamento de informações ganha um novo espaço caracterizado pelas redes sociais, que podem ser utilizadas para maximizar os níveis de bem-estar acadêmico.

Data mining and social networks processing method based on support vector machine and k-nearest neighbor

OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user’s micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users’ product inclination and provide accurate customer service requirements for enterprises.

Психологические особенности цифровой активности подростков на примере социальных сетей: обзор иностранных исследований

The article gives a review of new foreign studies describing the psychological characteristics of adolescent behavior in social networks. As a result of the analysis, it was possible to identify a number of positive and negative aspects of the use of social networks by adolescents. The positive aspects include the possibility of obtaining social support through social networks, and the simplification of the process of interpersonal communication. The negative aspects include manifestations of bullying by teenagers in relation to each other on social networks (“cyberbullying”), as well as the spread and use of psychoactive substances by teenagers using social networks, etc. A review of the “Bergen Social Media Addiction Scale” (BSMAS) was conducted. Тranslated into Russian forms and test key are presented.

Application of machine learning and cloud computing in social media behavior analysis

It is of great theoretical significance and practical value to analyze the characteristics of users and behaviors in social networks, to study the personalized recommendation algorithms of users, to explore the inherent laws of event development, and to predict the movement of information or opinions. This paper analyzes the Weibo behavior through machine learning and cloud computing technology. Moreover, this paper studies and analyzes traditional network algorithms, and proposes a microblog recommendation algorithm based on statistical features. At the same time, the research content of this paper focuses on microblog contents, user characteristics, user preferences, and influence levels. The algorithm has simple structure and strong computing performance and performs feature data mining through cloud computing big data method, which is suitable for online mining microblog behavior. In addition, the performance of the algorithm was analyzed by design comparison experiments. The research indicates that the research algorithm proposed in this paper has certain advantages, which can be applied to network behavior analysis mining, and can provide theoretical reference for subsequent related research.

Pedagogical Ethics in Social Media

The article examines the ethics of teacher’s behavior in social networks and formulates recommendations for teacher’s behavior on the Internet.

Pathological use and dependence on social networks — analysis from the position of phenomenology of addictive behavior

«Вестник Санкт-Петербургского университета. Психология» — научно-теоретический рецензируемый журнал, публикующий оригинальные статьи по различным отраслям психологической науки и адресованный профессиональному психологическому сообществу, а также ученым, работающим в смежных областях.

Identifying Experts in Community Question Answering Website Based on Graph Convolutional Neural Network

High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers. We propose two semi-supervised learning models based on the graph convolution neural network (GCN) to identify them. Both models comprehensively extract features from the social behavior network, user profiles, and question and answer text. Specifically, we construct a social behavior network according to the co-answering relationship among answerers, which means every two answerers are connected if they answer the same questions. The difference between these two models is the methods of extracting text features. One model named GCN-Doc uses Doc2vec to get text vectors before training. The other model named GCN-Lstm with a long short term memory (LSTM) network extracts text features while training. Experiments using real-world data from Zhihu.com, one of the largest Chinese CQA websites, show that both GCN-Doc and GCN-Lstm can identify experts effectively comparing with baselines of PageRank and other GCN based neural network models. Besides, GCN-Lstm performs better than GCN-Doc.

Relationships among social networking behaviors, depressive symptom and sedentary behaviors in older adults

Relationships among social networking behaviors, depressive symptom and sedentary behaviors in older adults

A new framework of electronic word-of-mouth in social networking sites: the system-based approach

With the advent of social media and online communities, electronic word of mouth (eWOM) marketing has gained increasing importance as an effective strategy in reducing the customers' uncertainty, increasing their confidence, knowledge acquisition of social customers as well as increasing their purchase intention. Many businesses have become aware of potential advantages of using WOM marketing approach in social networks. However, despite the appeal of the concept, few studies have been conducted to identify the phenomenon and its driving forces and consequences. Hence, the main aim of the current study is to provide a novel framework for eWOM marketing in social networking sites (SNSs) via a system-based approach which simultaneously identifies social WOM antecedents, its dimensions, and consequences. To answer the main research questions, comprehensive literature was reviewed along with focus group method, and then the extracted factors and related measures were evaluated and prioritised by experts. The proposed framework consists of antecedents of WOM marketing in social networking (including individual, organisational and environmental), the dimensions of WOM marketing behaviour (the type of eWOM, eWOM components, and its activities) in social networks as well as its consequences (consequences for customers and for organisations).

A new framework of electronic word-of-mouth in social networking sites: the system-based approach

With the advent of social media and online communities, electronic word of mouth (eWOM) marketing has gained increasing importance as an effective strategy in reducing the customers' uncertainty, increasing their confidence, knowledge acquisition of social customers as well as increasing their purchase intention. Many businesses have become aware of potential advantages of using WOM marketing approach in social networks. However, despite the appeal of the concept, few studies have been conducted to identify the phenomenon and its driving forces and consequences. Hence, the main aim of the current study is to provide a novel framework for eWOM marketing in social networking sites (SNSs) via a system-based approach which simultaneously identifies social WOM antecedents, its dimensions, and consequences. To answer the main research questions, comprehensive literature was reviewed along with focus group method, and then the extracted factors and related measures were evaluated and prioritised by experts. The proposed framework consists of antecedents of WOM marketing in social networking (including individual, organisational and environmental), the dimensions of WOM marketing behaviour (the type of eWOM, eWOM components, and its activities) in social networks as well as its consequences (consequences for customers and for organisations).

Patterns of social networking use and academic performance: Examining the link between quality and frequency of social networking use and academic performance among college-level students

Previous research has produced contrasting findings regarding the influence of social networking on academic performance. Many have found negative relationships but some have also demonstrated a positive effect for social networking on academic performance. Still others report no links between social networking and academic performance. Researchers have attempted to explain these divergences by specifying the quality and frequency of social networking habits and behaviors, distinguishing between adaptive and maladaptive traits. Yet it remains unclear how divergent use patterns are related to academic performance. To extend this literature, we examine the relationship between frequency, duration, and problematic (i.e., maladaptive) use of social networking sites on perceived performance expectancy and actual academic performance of a sample of college-level students recruited at a pre-university program in Northeastern Canada. Our findings suggest that different measures of SNS use and academic performance can yield different links between social networking and academic performance. Interestingly, we find that frequency and problematic use of SNS are related to performance expectancy though we do not find a concomitant effect for reported academic performance. Findings are discussed with respect to self-determination theory and implications for teaching and learning are offered as well.

Game theoretical inference of human behavior in social networks

Social networks emerge as a result of actors’ linking decisions. We propose a game-theoretical model of socio-strategic network formation on directed weighted graphs, in which every actors’ benefit is a parametric trade-off between centrality measure, brokerage opportunities, clustering coefficient, and sociological network patterns. We use two different stability definitions to infer individual behavior of homogeneous, rational agents from network structure, and to quantify the impact of cooperation. Our theoretical analysis confirms results known for specific network motifs studied previously in isolation, yet enables us to precisely quantify the trade-offs in the space of user preferences. To deal with complex networks of heterogeneous and irrational actors, we construct a statistical behavior estimation method using Nash equilibrium conditions. We provide evidence that our results are consistent with empirical, historical, and sociological observations on real-world data-sets. Furthermore, our method offers sociological and strategic interpretations of random networks models, such as preferential attachment and small-world networks.

Evolutionary dynamics of traveling behavior in social networks

In this paper, the topology of complex network is used to characterize the interactions among travelers. Based on the assumption of travelers’ limited rationality and the wisdom of crowds, an evolutionary game model of travelers was established. Combining these research findings, we study commuters’ travel behavior rules during peak traffic periods and make some recommendations that have the potential to relieve traffic congestion. In the first section, we analyze the effect of heterogeneity on travel choice behavior by Monte Carlo simulations, which explains why the total amount of traffic during peak hours is hard to decrease. The experimental results indicate that increasing fees would not reduce traffic flow during peak hours. Moreover, controlling price for the management of travel demand needs to be coordinated with the management of traffic system under the condition of limited road resources. On this foundation, the second section proposes an environmental travel mode for the transfer between cycle highways and rail transit. We verify the effectiveness of this travel mode in alleviating traffic congestion. Furthermore, considering the interactions between different travel modes in transportation system, we suggest some management recommendations with reference to the characteristics of different travel modes.