Prevalence Of Social Networks Research Articles

ОСОБЕННОСТИ ФОРМИРОВАНИЯ РЕПУТАЦИИ ВУЗА В УСЛОВИЯХ ПРИМЕНЕНИЯ СОВРЕМЕННЫХ ЦИФРОВЫХ ТЕХНОЛОГИЙ

In the era of digital transformation not only the commercial en- terprises business models are changing but also the universities ones, in par- ticular, their communication activities change. The article studies the current approaches of higher education institutions to forming a positive business rep- utation and managing reputational risks in the digital media field. It defines the expectations of various stakeholders from the activities of higher education institutions and also considers what universities expect from interaction with partners. Possible points of contact with target audiences are looked into, tak- ing into account such concepts as mission, brand, image and reputation of the university. The activity of several leading foreign universities in the Internet- environment, including social networks, as well as some other tools to form the reputation of the university are reviewed. It was found that despite the im- portance and prevalence of social networks in the world’s leading universities, they are not the only point of contact with target audiences and do not have a decisive role in shaping the reputation of the university. In particular, it was determined what important role publishing and library activities play for the image of the university. The reputational risks of universities in the digital age are also viewed. The article defines the necessity of marketing approach to the evaluation and formation of the university reputation.

Procrastination in Medical University Students with Different Personality Orientation

Introduction. The present article is devoted to the study of the procrastination severity in medical university students with different personality orientation, as well as to a comparative qualitative and quantitative analysis of this phenomenon. The aim of the current research is to investigate the procrastination severity in medical university students depending on the personality orientation. Methodology and research methods. The methodological basis of the study is the fundamental principles of the personality psychology, developed in the Russian psychology by A. N. Leontiev, S. L. Rubinstein. The personality orientation was considered on the basis of the provisions formulated by A. S. Makarenko, A. N. Leontiev, V. N. Myasishchev, K. K. Platonov. To understand the phenomenon of procrastination, the authors implemented the provisions articulated by Ya. I. Varvarycheva, E. L. Mikhailova, N. A. Shukhov. C. Lei’s procrastination scale was used in order to study the procrastination level. Personality orientation was examined with the use of the questionnaire developed by V. Smekal and M.Kucher.The obtained results were processed using the methods of mathematical statistics, in particular, the Kruskal–Wallis test, and Spearman’s rank correlation test. The sample included 96 students of the 1st-2nd years of education (average age is 19.5) of the Irkutsk State Medical University. Results. The distribution of medical students by the level of severity of procrastination is determined. It is demonstrated that this category of students is characterised by an average and high level of a partial or constant tendency to delay the implementation of important matters and making decisions, consciously postponing the implementation of leading activity. The business orientation of the personality, manifested in the predominance of motives associated with the activity itself and with the enthusiasm for the activity, dominates in students. It was estimated that students characterised by personal, business and interaction orientation are statistically significantly different by the severity of procrastination, while the students focused on communication and interaction are characterised by a higher level of procrastination. The prevalence of social networks, the availability of mobile communications and the Internet create the basis for the implementation of the communication need with the use of virtual communication, and simultaneously is an obstacle to the implementation of leading activities. For students with a personality orientation, it is most difficult to correlate their own needs and the need to care responsibilities, such a dissonance of tasks and leading motives activates procrastinating behaviour. Scientific novelty. The levels of procrastination among medical students are revealed. It is established that differences in the severity of procrastinating behaviour depends on the dominant orientation of the person (personal orientation, business orientation, interaction orientation). It is stated that students with an interaction orientation, inclined to show interest in people, attentiveness to interpersonal relations, have a higher level of procrastinating behaviour. It is revealed for the first time that the focus on communication with other people is a personal predictor of procrastinating behaviour among medical students. Practical significance. The research results allow for the determination of strategies and formulation of recommendations on correcting procrastination, as well as prevention of procrastination. The research findings can be employed to organise targeted psychological work with students of medical universities.

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

Building occupancy, one of the most important consequences of occupant behaviors, is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community. With the vast development of information technologies in the era of the internet-of-things, occupant sensing and data acquisition are not limited to a single node or traditional approaches. The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time. In this paper, we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks. The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media. On top of that, the typical building occupancy schedules are extracted with assumed people counting rules. The second approach utilizes the processed Global Positioning System (GPS) tracking data provided by social networking service companies such as Facebook and Google Maps. Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules. The results show that the extracted building occupancy schedules from different data sources (Twitter, Facebook, and Google Maps) share a similar trend but are slightly distinct from each other and hence may require further validation and corrections. To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media (TOSSM), data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM. The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.

Prevalence of social networks uses in disable̛ s and its effect on there mental health

Prevalence of social networks uses in disable̛ s and its effect on there mental health

For Better or Worse: Facebook Use and Wellbeing Among Community Mental Health Care Clients.

Online social networking is ubiquitous, but research regarding its relationship to wellbeing has yielded contradictory results. This study examined the relationship between Facebook use and wellbeing among community mental health service clients. Twenty-six clients from a community mental health service provided access to their Facebook page. Seven aspects of 3674 Facebook posts were coded, and cross-lagged multilevel models were estimated over three periods to establish the relationship between Facebook use and wellbeing over time. Some aspects of Facebook use were related to wellbeing within the same period, although this pattern of relationships did not emerge longitudinally. Although Facebook has the potential to forge social connections for those who are socially isolated, Facebook use was neither helpful nor harmful over time among people with mental health problems. Given the prevalence of social networking, a clearer understanding of its impact on wellbeing is critical for mental health providers.

Scalable Access Control For Privacy-Aware Media Sharing

The prevalence of social networks has made it easier than ever for users to share their photos, videos, and other media content with anybody from anywhere. However, the easy access of user-generated media content also brings about privacy concerns. Traditional access control mechanisms, where a single access policy is made for a specific piece of content, cannot satisfy the user privacy requirements in large-scale media sharing systems. Instead, configuring multiple levels of access privileges for the shared media content is desired. On one hand, it conforms to the principle of social networks in information propagation. On the other hand, it accords with the diverse and complex social relationship among social network users. In this paper, we propose a scalable media access control (SMAC) system to enable such a configuration in a secure and efficient manner. The proposed SMAC system is empowered by the scalable ciphertext policy attribute-based encryption algorithm as well as a comprehensive key-management scheme. We provide formal security proof to prove the security of the proposed SMAC system. In addition, we conduct extensive experiments on mobile devices to demonstrate its efficiency.

IRLT: Integrating Reputation and Local Trust for Trustworthy Service Recommendation in Service-Oriented Social Networks.

With the prevalence of Social Networks (SNs) and services, plenty of trust models for Trustworthy Service Recommendation (TSR) in Service-oriented SNs (S-SNs) have been proposed. The reputation-based schemes usually do not contain user preferences and are vulnerable to unfair rating attacks. Meanwhile, the local trust-based schemes generally have low reliability or even fail to work when the trust path is too long or does not exist. Thus it is beneficial to integrate them for TSR in S-SNs. This work improves the state-of-the-art Combining Global and Local Trust (CGLT) scheme and proposes a novel Integrating Reputation and Local Trust (IRLT) model which mainly includes four modules, namely Service Recommendation Interface (SRI) module, Local Trust-based Trust Evaluation (LTTE) module, Reputation-based Trust Evaluation (RTE) module and Aggregation Trust Evaluation (ATE) module. Besides, a synthetic S-SN based on the famous Advogato dataset is deployed and the well-known Discount Cumulative Gain (DCG) metric is employed to measure the service recommendation performance of our IRLT model with comparing to that of the excellent CGLT model. The results illustrate that our IRLT model is slightly superior to the CGLT model in honest environment and significantly outperforms the CGLT model in terms of the robustness against unfair rating attacks.

Application of Data Mining in Designing a Recommender System on Social Networks

The prevalence of social networks amongst people has become an inevitable issue. At the same time, social networks have widely been used for commercial purposes. As a result, in order to sell the products, social networks have been equipped with various recommender systems that provide the users with commercial offers that are appropriate for their behavior. The accuracy of the recommender systems in providing offers to the users and the number of offers accepted by the users are crucial issues. In the present study, a recommender system was designed that operates based on the users' behavior on Facebook and in two phases offers the users to buy their favorite products. In the first phase, the users' behavior is investigated and based on their interest they are offered to buy some products. In the second phase, the recommender system uses data mining techniques and provides the users with offers that are relevant to their previous purchase. The data of the study are factual and the results are valid. Moreover, the results indicate that the designed recommender system is highly accurate in providing offers to the users.

Social network-based service recommendation with trust enhancement

Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.

A secure K‐automorphism privacy preserving approach with high data utility in social networks

ABSTRACTThe prevalence of social networks has raised the concern for individual privacy leakage. Although preserving user privacy in social networks has been studied extensively, there is still a lot of room for improvement in the state of the art techniques. For example, the recently proposed K‐automorphism model can prevent node identity attack by constructing a K‐automorphism graph; however, it ignores preserving the privacy of the edges, resulting in path length leakage and edge leakage in the anonymous graph. Another example is the K‐isomorphism model, which was proposed to improve the K‐automorphism model by transforming the graph of social networks into K disjoined isomorphic subgraphs. However, it cuts off the connection among subgraphs, which impairs the data utility of the released anonymous graph. In this paper, we design a secure and high utility privacy preserving model, called AK‐secure, to prevent node identity attack, path length leakage, and edge leakage effectively. On the basis of the AK‐secure privacy preserving model, we propose a graph anonymous algorithm, which constructs an anonymous graph satisfying the AK‐secure privacy preserving model, minimizing information loss and guaranteeing high data utility. Extensive experiments on real data sets show the security, effectiveness of our AK‐secure privacy preserving model and the high data utility of the released anonymous graph. Copyright © 2013 John Wiley & Sons, Ltd.