Influential Incremental Learning-Based Privacy Preservation for Social Network Information

ABSTRACTThe immense popularity of social networks has overshadowed the potential concerns, especially related to information privacy. Despite huge debates among privacy experts in recent times, seminal works have explored information privacy concerns from the perspective of social network users. In response, this research aims to understand the impact of antecedents of users' information privacy concerns (UIPC) on privacy protection behavior (PPB) in social networks. Drawing from the Social Cognitive Theory and Protection Motivation Theory, the research model was analyzed using structural equation modeling. The empirical results show that perceived vulnerability, perceived severity, and self-efficacy significantly influences UIPC. However, rewards and response efficacy did not significantly contribute towards UIPC. The linkage between UIPC and PPB was found to be statistically significant. This research article will offer newer insights for conceptual development and assist the social network's providers in redesigning their privacy protection strategies for safeguarding users from privacy threats.

With the increasing development of online social networks, privacy protection attracts more and more concerns. Most research on privacy protection focus on specific techniques, and they assume users adopt security protection mechanisms for granted. The impact of user behaviors on the deployment of these mechanisms is usually neglected. In fact, the actual effect of privacy protection mechanisms depends heavily on user behaviors. Whether a user adopts the security mechanism is affected by incentives, interactions with friends and other factors. It is necessary to encourage users to take privacy protection settings in order to improve the security environment in social networks. Therefore, in this paper, a network evolutionary game model is proposed for studying the influence of user behaviors on the deployment of privacy protection mechanisms, where whether to take privacy settings is used as game strategies. Considering real interactions in social networks, games only happen between connected users in the model. Then the dynamic equation of user behaviors over time is derived. Based on the dynamic equation, evolutionary stability conditions are proven according to the benefit cost ratio of taking the protection. Experiments in real social networks verify the feasibility of the proposed model. The analysis results are helpful for guiding security services to promote the deployment of privacy protection mechanisms in social networks.

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose user's location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

Social Network Influences on Service Use Among Urban, African American Youth with Mental Health Problems

Most research on social networks is concerned with information transmission per se Our aim here is to supplement the social network perspective by incorporating mechanisms that govern social influence Research in social psychology suggests that individuals interact, in large part, to construct a shared reality that consists not only of shared information but also of agreed upon opinions. In this process, they do not simply transmit information, but more importantly, they influence one another to arrive at a common interpretation of information. We will discuss similarities and differences in how networks structure shapes the spread of information and governs social influence. Both simulation and empirical data concerning these two processes show that they operate in a very different way. The spread of information, described as a contagion process describes how individuals learn about new facts. Social influence process describes how individuals evaluate and weight different items of information and how they change their opinions and attitudes. The results of numerous experiments have shown that three critical factors determine the impact of social influence: (1) the number of sources exerting the influence, (2) the immediacy of the source(s) to the target(s), and (3) the strength of the source(s). The process by which humans construct social reality may prove informative for designing rules of interaction among intelligent agents. The primary implication of the present model is that information is not merely acquired, but also evaluated and negotiated in a social context. The process by which humans evaluate information and construct social reality may prove informative for designing rules of interaction among intelligent agents. The primary implication of the present model is that information is not merely acquired, but also evaluated and negotiated in a social context.

Addressing the issue of data leakage in social networks, this paper presents a classification of users' privacy information and introduces various personal data protection schemes utilizing blockchain technology. These schemes employ timestamp data storage within the blockchain, hash function anonymization techniques, and the Rivest-Shamir-Adleman (RSA) asymmetric encryption algorithm for encrypting and digitally signing transmitted data files. This innovative blockchain-based approach effectively tackles privacy leakage concerns in social networks and sets a benchmark for research in data security and social network safety. In this article, we propose a unique blockchain-based data protection scheme, specifically designed for different types of privacy leaks. This innovative solution addresses the pervasive problem of privacy leaks in social networks. However, current methods require more computational power during data interaction, which may hinder performance. Future research will concentrate on optimizing blockchain computational efficiency, aiming to develop a more robust data privacy protection system for blockchain-based social networks. By enhancing the efficiency and effectiveness of these privacy protection schemes, we hope to create a more secure environment for users to interact and share information on social platforms, ultimately fostering trust and confidence in digital social networks.

Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social networks suggests that players make non-optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being ‘rational’, we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. We hypothesise that bounded rationality of following a seed or adopting the strategy of a seed is negatively proportional to the distance from that node, and it follows that closeness centrality is the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying pay-offs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds, and the closeness centrality of the network provide the maximum negative influence. Further, we extend this model to a strategic decision-making scenario where each seed operates a strategy in a strategic game.

Relevance of the topic. Today, the role of marketing is to satisfy the consumer's curiosity through transparent and valuable content (Sawicki, 2016). More than 4 billion people around the world regularly use online services to find their products, entertainment, friends and even romances, and the behavior of customers and the way companies sell their products to both consumers and businesses has changed a lot (Chaffey & Ellis-Chadwick, 2019). As the use of the Internet has grown, so have related activities such as digital marketing. The main difference between traditional and digital marketing is that digital marketing focuses on the customer, offering him a personal message and a response to his request (Ištvanić, Crnjac Milić & Krpić, 2017). Digital marketing is an integral part of the digital business transformation process. It includes new marketing methods based on information and communication technologies. Todor (2015) the study is based on a study by ZenitOptimedia, which shows that internet use has increased significantly over the past five years, while the use of other media has decreased or only slightly increased. The situation is as follows: internet (+105 %.), outdoor advertising (+3%), television (-8%), cinema (-11%), radio (-15%), magazines (-23%), newspapers (-31%). For this reason, its application in practice is a prerequisite for the successful development of business in modern market conditions (Veleva & Tsvetanova, 2020).
 The problem. Football fans around the world are increasingly communicating with each other on social networks (Parganas et al., 2017), and football clubs, regardless of their degree of professionalism, include a large number of social media/media platforms for their online campaigns (Helleu, 2017). Bagchi, Sreepada (2020) in the survey conducted, respondents expressed their opinion about the social networks of their favorite football club and why they follow the news of the club in them. Especially during the COVID-19 pandemic, the influence of social networks on football has increased. The clubs tried to give interesting and new content to the fans. Some organized social media quizzes, others made videos with a message that the stadium missed the fans (Szczepkowski, 2021).
 The aim of the study is to analyze the influence of digital marketing on the communication of football clubs in the case of Lithuania.
 The subject of the study is the influence of digital marketing on the communication of football clubs.
 The choice of assessing the influence of electronic marketing on the communication of football clubs was determined by the popularity of the sport in the world, the constantly growing sports market and a large fan base. The quantitative research method chosen for the survey is a questionnaire survey. The quantitative study examined the opinions and behavior of fans of Lithuanian football clubs – consumers – related to the applied online marketing tools. In order to assess the influence of digital marketing on the communication of football clubs, literary analysis and empirical research were chosen. The questionnaire was compiled on the basis of Vale & Fernandes (2018) “Social media and sports: driving fan engagement with football clubs on Facebook” research. To obtain a targeted sample of those who responded in the study, paniotto's sample size formula (Bokor et al., 2011) was used to avoid going beyond the 5 percent error. A total of 451 respondents participated in the survey.
 Results and conclusion. The largest part of football clubs follow with the help of social networks and even note that they monitor the news on a daily basis thanks to social media platforms. All categories of questions were dominated by the influence of social networks on the communication of the football club. From the results of the study, it is noted that clubs use various strategies, but dominate and fans like social networks the most. Their rating from the presented means was the best. In addition, the respondents were quite unanimous in answering the questions of involvement in the activities of the community - they agree that the activities of the supported football club are important to them. The vast majority of the study participants are happy to get involved in the activities of the communities of clubs and organizations, which give them good emotions, relax them, help them escape from the routine and reduce the feeling of loneliness. The vast majority emphasize that they see basic information about the activities of the clubs there. Also on the Internet buys club products (tickets, merchandise), which also shows that digitalization has progressed in the football world. In conclusion, it can be noted that social networks dominated throughout the study, both on the part of football organizations – communication is developed thanks to this tool and from the part of fans – they recognize it as the most effective tool.
 Keywords: digital marketing in football, football club communication, A League

The Quest for Information Privacy in Africa: A Review Essay

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of “Internet plus medical and health” has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected.

Influence maximization, defined as finding a small subset of nodes that maximizes spread of influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear Threshold (LT) models, where many greedy-based algorithms have been proposed with the best approximation guarantee. However, existing greedy-based algorithms are inefficient on large networks, as it demands heavy Monte-Carlo simulations of the spread functions for each node at the initial step [7] . In this paper, we establish new upper bounds to significantly reduce the number of Monte-Carlo simulations in greedy-based algorithms, especially at the initial step. We theoretically prove that the bound is tight and convergent when the summation of weights towards (or from) each node is less than 1. Based on the bound, we propose a new Upper Bound based Lazy Forward algorithm ( UBLF in short) for discovering the top-k influential nodes in social networks. We test and compare UBLF with prior greedy algorithms, especially CELF [30] . Experimental results show that UBLF reduces more than 95 percent Monte-Carlo simulations of CELF and achieves about $2\hbox{-}10$ times speedup when the seed set is small.

Understanding how information flows in online social networks is of great importance. It is generally difficult to obtain accurate prediction results of cascades over such networks, therefore a variety of diffusion models have been proposed in the literature to simulate diffusion processes instead. We argue that such models require extensive simulation results to produce good estimates of future spreads. In this work, we take a complimentary approach. We present a generalized, analytical model of influence in social networks that captures social influence at various levels of granularity, ranging from pairwise influence, to local neighborhood, to the general population, and external events, therefore capturing the complex dynamics of human behavior. We demonstrate that our model can integrate a variety of diffusion models. Particularly, we show that commonly used diffusion models in social networks can be reduced to special cases of our model, by carefully defining their parameters. Our goal is to provide a closed-form expression to approximate the probability of infection for every node in an arbitrary, directed network at any time t. We quantitatively evaluate the approximation quality of our analytical solution as compared to numerous popular diffusion models on a real-world dataset and a series of synthetic graphs.

Indecent years, The massive growth of data in various fields not only promotes the development of data mining algorithm, But also brings the threat of privacy disclosure to the storage and mining of user data. In the process of data mining, protecting users' privacy, designing data mining methods that support privacy protection has become an important research field. Focusing on the hidden dangers of privacy disclosure in data mining methods, This paper mainly introduces the latest research progress of three privacy protection technologies, namely data distortion technology, data encryption technology and restricted publishing technology, and summarizes the research trend of data mining methods supporting privacy protection in the field of image processing and natural language processing.

Over the past 20 years, researchers have shown increasing interest in social network analysis to understand second language acquisition (SLA), especially in a study abroad (SA) context. To date, few longitudinal studies have examined the joint evolution of the learners’ sociolinguistic competence and socialisation during the SA. By shifting the focus from a global view of the study abroad context to a deep analysis of the composition and structure of each learner’ social networks in the host country, we aim to provide a better understanding of the development of sociolinguistic competence in SLA (Gautier & Chevrot, 2015). We apply the sociological concept of a social network to sociolinguistics. To explore the sociolinguistic competence of 29 learners, we focus on two well-described sociolinguistic variables in French: the optional liaison and the negative ne. We also gathered data on their social networks and provided a deep analysis of each participant’s network. We implemented a quantitative approach to analyse and depict the social networks of the learners. Statistically significant relationships were found between changes in the learners’ personal network and their use of the two sociolinguistic variables. The development of L2-oriented social networks (in terms of size, speaking time, and frequency) over nine months of the SA helps learners to reduce their use of standard variants. Conversely, the development of L1-oriented social networks during the SA is associated with greater use of standard variants.

The article describes computational experiment and further research work in the area of identification of destructive information influence in social networks. The problem of distribution of suicidal content via open sources is presented. On the basis of calculations there was made a conclusions about the prospects of using the methods of information retrieval in the task of identification of the destructive informational influence. The procedure of singular value decomposition of the matrix pattern-message is considered as a path of the future research.