Number Of Social Network Users Research Articles

Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy

As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections imperceptibly so as to protect the privacy of users. The existing evolutionary models have some successes in this type of NP-hard problem but can only be applied to small-scale datasets, rendering them inadequate for real-world applications. To alleviate this problem, we propose an original and novel CoeCo, a cooperative evolutionary community obfuscation model. In CoeCo, we leverage the divide-and-conquer strategy and put forward a co-evolutionary optimization algorithm suitable for community structure, in which two different fitness functions promote each other to find the optimal edge set. In addition, the motif hypergraph and permanence are used to improve population initialization. The experimental results indicate that our proposed method can achieve excellent efficacy in obfuscating community structure and also greatly reduces running time.

Social Network Recommendations for Friends with Neo4j Graph Database

In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.

Social Network Recommendations for Friends with Neo4j Graph Database

In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.

Mapping Homophobia and Transphobia on Social Media

IntroductionOne of the consequences of the increase in the number of social network users has been the inappropriate use of social networks by some of these users. Hate speeches are frequently identified on social media, and these promote certain homophobic and transphobic attitudes, causing psychological consequences on users belonging to minority gender groups. With this work, it is intended to know the current state of the problem raised, to facilitate the activity of new researchers in an emerging field.MethodologyBibliographic analysis of 203 papers from the Scopus databases for the period between 1997 and 2022 using the VOSViewer software. The search for publications was carried out in February 2023.ResultsThere is a positive trend in the number of relevant publications since 2017, mainly in 2021 and 2022. The research on homophobia and transphobia on social media in USA is prominent, with a high number of published articles, productive organizations, and influential authors. Twitter is shown to be the social network most widely used to spread homotransphobic hate speech. Environments conducive to the development of homotransphobic attitudes are identified as collective sports, mainly football and its supporters, as well as peer groups.ConclusionsIt is a growing problem that requires intervention at the societal level, requiring the development of legislation that moves away from heteronormativity, the development of mechanisms for automatic detection of homotransphobic discourse on social networks, and a multidisciplinary analysis and approach to control the problem as well as provide adequate social support to affected groups.

Value Creation and Capture with Big Data in Smart Phones Companies

With the advent of social media, the volume of data generated on the internet has exploded due to the growing number of social network users and their interactions on the internet. Given that, in the age of the digital economy, data has become raw material in terms of decision making, it is important and urgent to conduct a study to understand the effect of big data on value creation and value capture. The goal of the current research is to study the share of big data in value creation and capture in the companies Apple and Samsung. The main question addressed by this article is whether the increasing volumes of data in the digital age can improve the creation and capture of value. To achieve this goal, we considered active users of the three main social networks that Samsung and Apple companies use for their advertising to describe “big data”. We measure the “value creation” through the hardware component of the smartphone, such as the battery, camera, CPU speed, RAM (Random-Access Memory), screen size, and storage. Profit, satisfaction, and unit of phone sold are the three manifest variables considered to measure “Value capture”. As a methodology, we used partial least squares and structural equation modeling to obtain the results. The pattern enables us to measure the effect of big data on value creation and value capture. The results indicate that CPU speed, RAMs, and battery capacity are the principal variables that impact the “value creation” in terms of customer need and satisfaction.

Current Topics in Health Care Law

Current Topics in Health Care Law

СОЦІАЛЬНІ МЕРЕЖІ ЯК КАТАЛІЗАТОРИ ДЕМОКРАТИЧНИХ ПРОЦЕСІВ У СУСПІЛЬСТВІ

<p><strong><em>The purpose</em></strong><em> of the study is to conduct a detailed scientific and practical substantiation of the features of social networks as catalysts for democratic processes in society due to modern conditions in the framework of globalization processes.</em></p><p><strong><em>Research methodology.</em></strong><em> The study used general scientific (generalization, comparison, induction and deduction) and empirical-theoretical methods (analysis, synthesis). The use of system-structural and statistical analysis made it possible to differentiate the main ratings of social network platforms, the number of social network users for the period 2012–2022 and the penetration of social networks by region in January 2022. Based on the survey, it was determined which social network citizens of Ukraine use to receive news.</em></p><p><strong><em>Results.</em></strong><em> It is determined that social networks are becoming a global phenomenon of communication and are developing independently and in an extremely dynamic environment of new innovative technologies. It is established that social networks are a segment of communication and socialization, which influences the formation of new social structures. It is determined that social networks are part of the public discourse, which allows to directly and publicly respond to political processes in society, which opens the possibility of political influence. The analysis found that the use of social networks is far from evenly distributed around the world, and the total number of social network users around the world has almost doubled from 1.48 billion in 2012 to 4.62 billion in 2022.</em></p><p><strong><em>Novelty. </em></strong><em>The scientific novelty of the results obtained in the article is due to the solution of an important scientific problem, which is to develop theoretical principles and practical recommendations for analyzing the features of social networks as catalysts for democratic processes in society. The article further develops the study on the main indicators of the spread of social networks in the global and national dimensions.</em></p><p><strong><em>Practical significance. </em></strong><em>The results of the study can be used in the study of the scientific field of «Social Communications». The results can be directed to further research of social networks as catalysts of democratic processes in society.</em></p><p><strong><em>Key words:</em></strong><em> social networks, catalysts, democratic processes, society.</em></p>

Цифровий маркетинг та соціальні мережі

The rapid development of digital communication technologies, the spread of Internet access to the population, the growing number of smartphones, increasing the number of social network users and other factors are the reason for the sharp growth of online operations and services in recent years. In 2020, the Covid-19 epidemic was added to this list of factors, creating an additional impetus for the growth of online business. This situation causes significant changes in the implementation of marketing activities by enterprises. Digital marketing is an important tool that allows companies to achieve their marketing goals, as well as influence a large number of people at a relatively low cost. More and more consumers are spending a significant amount of time on the Internet searching for information, products and services, communicating with other consumers about their experience and interaction with companies. The purpose of this article is to substantiate the theoretical and practical provisions of the use of digital marketing in the enterprise, to characterize the possibilities of using social networks as a tool of digital marketing, taking into account their features, limitations and advantages. The article substantiates that in modern conditions it is not enough to have only a website for the enterprise. Social networks play an increasingly important role not only in people’s lives, but also in business activities, which is an additional but important way to attract the attention of potential customers and promote trust. The most effective ways to use Facebook as a marketing tool are analyzed (direct advertising, social posting, detailed audience analysis, link sharing). It also describes a number of features that should be considered for forming a digital marketing strategy in 2021 and 2022. It is concluded that marketing in social networks can be a valuable tool that will help create an Internet brand awareness for the company, increase trust in it and develop the demand for goods and services.

Guest Editorial Introduction to the Special Section on Artificial Intelligence For Social Networks

The articles in this special section focus on artificial intelligence for social networks. Such networks Facebook, Twitter, and Instagram have become very popular in recent years and are extensively used by users to connect and communicate with their friends. Due to the increasing number of social network users and the various use cases that the applications are created for, huge volume of data are being generated, for example, Facebook has over 1.23 billion monthly active users and Twitter generates 500 million tweets per day and around 200 billion tweets per year. The huge amount of available data on social platforms can help improve people’s life quality and benefit various interest groups like public agencies, companies, advertisers, application developers, and data-mining researchers. Therefore, researchers in academia and industry have been advancing the theory, operation, and applications of social networks. Luckily for swamped social media researchers, new technology enables brands to extract useful knowledge using artificial intelligence. The rapid developments in AI technology encounter great opportunities as well as challenges for social networks. AI approaches are considered as a good candidate, as they have already been proven to be successful in other information-intensive and knowledge-critical domains.

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.

Effective care training for patients with COVID-19 through social network

ABSTRACT This study explores effective care training factors for patients with coronavirus disease 2019 (COVID-19) through social network message combining qualitative and quantitative methods. The research was conducted in two phases. In the first phase, based on the theoretical saturation approach, active social networking audiences were sampled and questioned in the field of COVID-19 learning. In this regard, 38 audiences interviewed and based on thematic analysis 20 non-repetitive features (basic them), five organizing themes, and a thematic network extracted. In the second phase, 11 experts were used to rank these extracted basic and organizing themes using the DEMATEL as a quantitative method. Finally, the results were tested using a one-sample T-test and Spearman correlation by a more significant number of social network users. The thematic analysis results introduce 20 basic themes, five organizing themes, and network themes for effective care training factors for patients with COVID-19 through the social network. Indeed, it can be concluded that the performed network themes and DEMATEL entails a unique model in this context. This model shows useful messages for COVID-19 training in the social network under the influence of five factors: simplicity, multimedia, validity, availability, and generalization.

The impact of social media on sport development in Azerbaijan

In many countries of the world, social media platforms play an important role in people's lives. People can easily access online the information they need. As the number of social network users grows, the spheres affected by social media increases as well. One of these is sport.

THE ROLE OF SOCIAL NETWORKS IN THE CREATION OF THE TOURISTS` ECOLOGICAL CONSCIOUSNESS

More and more clear consequences that arise in the environment due to irrational use of natural resources have made sustainable development today important. In tourism, sustainable development is also imposed as a priority and necessity, but in practice it often encounters obstacles to its implementation. The problems that arise in applying the principles of sustainable development in tourism can be a result of the conflicting interests of the key stakeholders. To avoid this, there is a need to establish an adequate system of communication between all stakeholders. An important group of stakeholders in the sustainable development of tourism are tourists who, due to their stay in a tourist destination, can achieve positive and negative environmental impacts. In order to suppress their negative environmental implications, there should be a way to influence the creation or the increase of their ecological awareness. The heterogeneity of tourist demand, i.e. different wishes, habits, attitudes, opinions, needs and beliefs of tourists makes this task difficult. Creating ethical codes and establishing codes of conduct for tourists while staying in a tourist destination can significantly contribute to sustainable development, however, it is necessary to find an adequate medium through which the message on the importance and necessity of environmental protection will be transferred in the right way and at the right time to tourists. The aim of the paper is to show the role that social networks can have in creating the ecological awareness of tourists. By developing modern technologies, social networks have become the medium through which a person most often communicates with the others, where one records information about him/herself and where one can get information about others. In tourism social networks play an important role in creating marketing strategies, because they represent an instrument that allows the destination to interact with tourists and to find out and observe their opinions, attitudes and evaluations of services in tourism. Through social networks, tourists find inspiration for new travel, share tips and experiences with other users, share travel photos, and they can be informed about the tourist destination offer. Researches show that there is an increasing number of social network users and that they spend a significant part of the day using them. By analyzing the role that social networks have in tourism marketing, it can be concluded that they are a medium by which messages that can influence the increase of the tourists` ecological awareness can be created and by which the messages about the codes of ethics and rules of tourists behavior can be transmitted even before the tourists travel to the destination. The specificity of social networks can make this message more durable and created in a way that is consistent with the heterogeneous characteristics of the tourism market. Nevertheless, in using social networks for creating tourists` ecological awareness, there should be a careful approach and there is a need for additional research on how tourists or potential tourists use social networks.

Identification and characterisation of Facebook user profiles considering interaction aspects

ABSTRACTThe great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify different user behaviours using clustering techniques. We found three different user profiles regarding interactions performed in the social network: viewer, participant and content producer. Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The 'viewer' mainly observes what happens in the network. The ‘participant’ interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant profile are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that ‘content producer’ users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two profiles. Finally, we also verify that the profiles are not mutually exclusive, that is, the user of a profile can at determined moment perform the behaviour of another profile.

Identification and characterisation of Facebook user profiles considering interaction aspects

The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we u...

Medical Information Extraction Model for User-generated Content.

Introduction:The number of social network users is on the rise, and the size of the user-generated contents is increasing as well. Analyzing the generated contents can lead to the attainment of a vast amount of information, such as users’ feelings on specific products or events, or personal information about life events.Aim:The aim of this paper is to describe an model for detecting medical information present in generated contents, such as posts or comments.Results:The proposed model is based on the Unified Medical Language System (UMLS) and is tested on a dataset collected from Twitter and Facebook. The extracted information can be used to aid in the early detection of diseases or to supply commercial benefits to medical companies. Experimental results demonstrate that the proposed model achieves 94.6% accuracy and 87% precision.Conclusion:In this study, we attempted to extract clinical information present in UGC. Using the proposed model should involve a reliable dataset that contains most clinical expressions; the UMLS was a suitable dataset for our model.

Boosted Constrained K-Means Algorithm for Social Networks Circles Analysis

The volume of information generated by a huge number of social networks users is increasing every day. Social networks analysis has gained intensive attention in the data mining research community to identify circles of users depending on the characteristics in the individual profiles or the structure of the network. In this paper, we propose the boosting principle to find the circles of social networks. Constrained k-means clustering method is used as a weak learner with the boosting framework. This method generates a constrained clustering represented by a kernel matrix according to the priorities of the pair-wise constraints. The experimental results show that the proposed algorithm using boosting principle for social network analysis improves the performance of the clustering and outperforms the state-of-the-art.

Health, one of Campofrío's brand values on Twitter

Abstract In the last few years, a significant increase in the number of social network users has garnered enough attention from the business sector to include these collaborative environments in their advertising strategies. To show this reality, this study focuses on the Campofrio's Spanish Twitter account that has radically changed their advertising discourse since 2012. While maintaining their image as a brand focused on healthy eating, they added the value of a positive outlook on life which, in turn, brings physical and mental wellbeing.

트위터를 이용한 이벤트 감지 시스템

최근 소셜 네트워크 사용자들이 늘어나면서, 각 지역에서 관심 받고 있는 사회적인 이슈나 재해 등과 같은 이벤트에 대한 정보들이 소셜 미디어 사이트를 통해 실시간으로 빠르게 대량으로 게시되고 있으며, 사회적 파급효과도 매우 커지고 있다. 본 논문에서는 지역정보를 가진 트위터 데이터를 이용하여 특정 시간, 지역에 사용자들이 관심을 가지고 있는 이벤트를 탐지하는 방법을 제안하고자 한다. 이를 위해 트위터 스트리밍 API를 이용해 데이터를 수집하고, 트윗의 키워드들의 시간에 따른 빈도수를 분석하여 정상적인 패턴과 다른 패턴을 가진 키워드를 이벤트로 추출하고, 같은 이벤트에 대한 키워드들을 군집화 하기 위해 co-occurrence 그래프를 이용하여 이벤트 감지 시스템을 구현하였다. 그리고 실험을 통해 제안한 기법의 유효성을 검증한다. As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

Automatic rule generation using crowdsourcing for better relationship type discovery

With the increasing popularity of information sharing and the growing number of social network users, relationship management is one of the key challenges which arise in the context of social networks. One particular relationship management task aims at identifying relationship types that are relevant between social network users and their contacts. Manually identifying relationship types is one possible solution, however it is a time-consuming and tedious task that requires constant maintenance. In this paper, we present a rule-based approach that sets the focus on published photos as a valuable source to identify relationship types. Our approach automatically generates relevant relationship discovery rules based on a crowdsourcing methodology that constructs useful photo datasets. Knowledge is first retrieved from these datasets and then used to create relationship discovery rules. The obtained set of rules is extended using a number of predefined common sense rules and then personalized using a rule mining algorithm. Experimental results demonstrate the correctness and the efficiency of the generated sets of rules to identify relationship types.