Implicit Community Discovery Based on Microblog Theme Homogeneity

This chapter explores the topic of virtual community discovery in social networks. It starts with defining virtual communities. After that, the chapter goes over several network modelling strategies and community identification techniques. The significance of standardised datasets in evaluating algorithm performance is shown by a thorough analysis of benchmark networks used to test community detection techniques. The accuracy indices for assessing virtual community identification algorithms are also covered. The chapter offers common datasets needed to evaluate virtual community discovery techniques. Next, it explores community discovery using the Girvan-Newman and Louvain algorithms, two well-known techniques, to demonstrate practical applications. These illustrations explain how theoretical ideas can be applied in practical settings. Finally, this chapter provides a thorough overview of the concepts and assessment criteria for virtual community discovery in social networks, providing insightful information for both scholars and industry professionals.

The distribution of user community in social network has the problem of similar feature distribution, which leads to the high viscosity between community and community, and it is difficult to mine community features. A social network community discovery algorithm is proposed based on parallel recommendation. The fuzzy decision model of social network community discovery is established by taking the user behavior value, user consumption value and loyalty of social network as independent variables. The relationship between the interaction degree and the recommendation effect in the social network is analyzed by extracting the characteristic quantity of the community association attribute in the social network, and the community group recommendation in the social network is carried out by using the parallel recommendation algorithm, and the number of visits between users is analyzed. The number of messages is used as the weighted weight coefficient, the difference factor is introduced to evaluate the influence of the community, the synchronous label of the social network community is established, and the community feature mining and parallel recommendation are realized according to the label location. The simulation results show that, the proposed method has good accuracy in community discovery, high accuracy in community attribute feature mining, and low cost of the algorithm. It has obvious advantages compared with similar algorithms.

The article is devoted to the problem of increasing the adolescents’ involvement in virtual cyberspace and the influence of activities in it on their social and psychological characteristics, as well as on the features of the critical brain systems functioning. Close and highest values of use by teenagers the frequency as a means of leisure video games and communication in social networks are considered. At the same time, the increase in the number of users with new technologies, according to modern scientific literature, outpaces the accumulation of knowledge about the influence of the information environment on the human psyche and brain, which are forming and developing in new conditions. There are evidence, provided the high values of the use two main types of digital technologies: virtual communications in social networks and video games, which can be associated with the methodical features of surveys and questionnaires and do not reflect the selectivity of adolescent behavior in cyberspace. Scientific sources suggest that a certain proportion of researchers are positive and optimistic about the use of video games in education and leisure, emphasizing their evolving effects on visual memory, spatial orientation and the ability to make correct decisions in uncertain or complex situations. At the same time, there is little evidence of significant changes in verbal-conceptual intelligence. The structure of intellectual functions changes with an emphasis on non-verbal intelligence clearly demonstrate the directional impact of computer games on the highest mental functions, the degree transformation of which depends on the game practice and content of games. The noted negative effects of adolescent gaming activity on the emotional and social characteristics of gamers’ behavior can be associated not only with the gaming time so perhaps with the premorbid features of adolescents, such as depressive experiences. Most researchers on the impact of over-the-counter social media on the psychological and social characteristics of adolescents express similar views, characterizing fans of virtual communication as aggressive, hostile personalities, with superficial assessments and reduced criticism of information in blogs and accounts. This set of properties of users by social networks creates a basis for their involvement in various anti-social actions in cyberspace and real public life. In connection with these facts, it is necessary and possible to research the role of social networks’ commercialization as the causes of the emergence of underage millionaires, bloggers, whose example and success educates much more effectively than school and family. Clinical study over the fascination with video games and virtual life in social networks are in DSM-V in the form of a special form of technology dependences, internet- addiction. Internet abuses based of the specifics of the activity of neural networks, regulating higher mental functions and behaviors. There is the risk of organic lesions of the central nervous system of modern adolescents and social disability of the younger generation. At present, it is not clear the cause-and-effect relationship of premorbid features of abuser and the rate of addiction formation, which certainly requires further research of adolescents, whose leisure time is increase to many hours of action on the Internet and gadgets. The facts mentioned in the article emphasize the relevance and importance of studying psychophysiological and physiological mechanisms, which are most sensitive to the influence of information factors of the environment. It is necessary to formulate a general practical task of preventing excessive fascination with the Internet and gaming by teenagers, the formation of digital diagnostic methods of assessments of brain and mental functioning, as well as corrective psychological and educational activities and programs.

The paper considers marketing communications in social networks that allow to constantly maintain two-way feedback from consumers. Authors understand social media marketing as the use of existing platforms to raise brand awareness among consumers by using the principles of these platforms. Forms of platform promotion are defined: traditional and social (organic). The paper analyzes and systematizes the most popular social networks, which include: Facebook, Whatsapp, Youtube, Instagram, Tik Tok, Twitter, LinkedIn, Pinterest, Snapchat. The considered social networks are divided into: social networks, professional networks, video hosting, knowledge sharing media, micro-blogs and user forums. The paper presents the TOP social networks in the world as of 2019. The paper highlights and substantiates the benefits of marketing in social media, namely: brand building, customer loyalty, conversion, customer service, awareness raising and branding; customer base growth; attracting traffic, leads and sales, etc. As social networks promote active communication between companies and consumers, stimulate interaction among users - the factors that influence customer engagement are identified in the work; it was investigated which content on the brand's page on social networks attracts the majority and makes you want to visit again. It is justified that when using SMM (SocialMediaMarketing) you need to pay more attention to the published information, as this is one of the main factors influencing customers. The paper emphasizes the importance of openness as the main factor in creating a trusting relationship "brand-consumer", which in turn shapes the behavior of the buyer in reality. SMM has been proven to be a powerful media platform for customer communication, brand building, sales growth and website traffic, as the brand demonstrates its openness to communication through social media. It is substantiated that with the help of social networks brands remain in trend.

Young Cancer on <em>Instagram</em>

The aim of this study is to reveal Open Education related social media usage in Turkey through social network analyses. To this end, the most widely used social media network in Turkey, Facebook, was chosen. All the pages and groups created on Facebook related to Open Education were found. A total of 207 groups and 521 pages were accessed and entered into the social network analysis. Within the scope of this study, the density and centrality features of the network were analyzed for groups whereas the density, centrality, and degrees features of the network were analyzed for pages. The study's results revealed that while pages and groups related to programs emphasizing current job opportunities and student support services were more actively used, commercial accounts attempted to exploit students' exam anxiety instead of generating actual content. The findings also revealed that well structured institutional pages appeal to students and have the potential to reach high levels of popular in short periods of time.

Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com). In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of those social networking media such as Hotel, Villa, SPA, restaurants, travel agents, airlines, media, tourist attraction management has a good rating result, and it has represented that the image of Bali tourism has a good rank in the eyes of local and international tourists. This data was also strengthened by the existence of empirical studies where there were the data from a hundred tourists came to Bali both locally and internationally has filled out the questionnaire about Bali in order to obtain maximum results on the image of Bali tourism. The results showed that, social networking media were effectively used in this study. Many companies in Bali used facebook and twitter. In addition, Bali Tourism image placed in positive rank on both of social networking sites, and it was gained by the results of questionnaires spread towards the visitor.

Addressing the policy challenges and opportunities of “Big data”

for many users nowadays social media plays a big role in their world, as it helps to connect with new people while keeping the old bonds intact. The drawback is that we can’t able to see that, the data is being generated every minute of a second and that data is kept secure in the hands of a few tech giants, who are using it and selling it as they see fit and using it in a manner as they see profitable and with this paper I’d like to propose that shifting social media from host-centric to the information-centric network on the blockchain would not only be safer yet convenient and secured.In peer-to-peer system, cryptocurrency is used to pay for and secure content acquisition.The blockchain lets individuals acquire ownership over their Data, Property and Assets. In social networking, a decentralised approach to blockchain technology protects privacy and allows for e-commerce, ledger based crowd funding, smart apps, and smart contracts. Platforms like Facebook, LinkedIn, Instagram, Twitter, Reddit, and other digital social network media platforms offer free for all access, which is convenient in the name of connecting and socialising but they are collecting are data and using it to sell it to other third party buyers for the price of our privacySome of the most important difficulties confronting social media network business period, profit is focused on advertising and analytics. False propaganda based information on (Facebook), rampant bullying and harassment (Twitter), single party controlled censorship, and demonetization are some of the most serious issues facing social networking (YouTube).

The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social networks. The significant overlaps among communities make most existing community detection algorithms ineffective. The lack of effective and efficient tools resulted in very few empirical studies on large-scale detection and analyses of overlapping community structure in real social networks. We developed recently a scalable and accurate method called the Partial Community Merger Algorithm (PCMA) with linear complexity and demonstrated its effectiveness by analyzing two online social networks, Sina Weibo and Friendster, with 79.4 and 65.6 million vertices, respectively. Here, we report in-depth analyses of the 2.9 million communities detected by PCMA to uncover their complex overlapping structure. Each community usually overlaps with a significant number of other communities and has far more outbound edges than internal edges. Yet, the communities remain well separated from each other. Most vertices in a community are multi-membership vertices, and they can be at the core or the peripheral. Almost half of the entire network can be accounted for by an extremely dense network of communities, with the communities being the vertices and the overlaps being the edges. The empirical findings ask for rethinking the notion of community, especially the boundary of a community. Realizing that it is how the edges are organized that matters, the f-core is suggested as a suitable concept for overlapping community in social networks. The results shed new light on the understanding of overlapping community.

The article is devoted to the topical issue of identifying information threats in virtual communities of social networks and mechanisms for counteracting such threats. The process of organization of monitoring and content analysis of virtual communities in social networks is studied. The technology of detecting information threats of virtual communities in social networks and methods of their assessment have been developed. An approach to finding pages of discussions on social networks based on the Louvain method is proposed. The technology of determining the degree of information threat of the virtual community in social networks and ways to counteract the information threats of the virtual community have been developed.

In view of that most of the current community discovery methods in social network do not consider node self-transfer and node bias, so that it is not possible to extract the graph features effectively, which leads to the ineffective problem by the community discovery, this paper proposes a novel social network community discovery algorithm (Local Distance Laplace, LDL). First, a Laplace matrix decomposition model is constructed based on the principle of matrix decomposition. Secondly, considering the high cost of global social network information acquisition and calculation, a community discovery model based on local distance is proposed. Finally, the optimal community structure is selected by using the NRO (Node Rank Optimization) function. A comprehensive comparative analysis is made on eleven real and synthetic networks. At the same time, validation analysis is conducted on eleven different social networks (Karate, Dolphins, Lemis, Public book, Football, Celegansnertal, Email, Public blogs, Netscience, Power, Hep_th). The experimental simulation results show that: in the real network, the proposed LDL algorithm improved the overall performance by nearly 7% compared with the seven state of art optimal methods (CoVeC, EDBC, JNMF, EADP, LPANNI, LSA, SCFS). The novel algorithm is reasonable and effective and it can also be extended to multi-scale community discovery.

Social media has become a constant presence in the daily lives of young people. As a result, many educational procedures and processes have been influenced by the widespread use of social networks. The number of people using social media has continuously increased. Students’ perceptions on how social media affects their learning is a prominent subject of research. Students’ school achievement has been significantly impacted by their interaction with professors, peers, and online knowledge-sharing behaviors. Several aspects are involved in this review, which aims to shed light on how students use social media in higher education. Findings reveal that the usage of online social media for cooperative learning encourages students to be innovative, dynamic, and research oriented. In other words, it is a matter of academic competence. This study gathered and analyzed social media data in higher education for computing disciplines. Digital libraries from EBSCO, IEEE, and ACM were utilized for data gathering. All of the above traits were found to be positively influenced by social networks, which suggest that their usage in higher education should be broadened. Teachers and educational institutions have not taken full use of internet social networks. Keywords: social media; social networks; higher institutions education

Social communication networks created during the 2018 earthquake sequence in Lombok, Indonesia using social network data from a social media platform, i.e., Facebook, and surveys in local communities were analyzed. The different modes of communication networks used during the disaster event and post-disaster mitigation were examined. Data on social media platform usage were collected from the Facebook page of the Agency for Meteorology, Climatology and Geophysics of Indonesia and the National Board for Disaster Management of Indonesia. The data show large clusters of social relation networks based on dominant issues discussed by the public under several trending hashtags. Meanwhile, the local patterns of social communication networks, particularly the flow of information and communication used by the local communities of the impacted villages in Lombok, differ depending on the culture of communication and time of accessing information. During the 2018 Lombok earthquake sequence, communication was predominantly sought from community leaders, religious leaders, family members, and neighbours. This study highlights the implementation of a decentralization system in Indonesia, which has given the local and regional authorities a significant role in disaster management, response, and emergency command for the local and regional community, instead of relying largely on the response of the central Jakarta government.

The objective of this study is to discover social communities in a social network using different social network community discovery methods that utilize metrics and structures like degree, clustering coefficient, k-cores, weak and strong components. We have used two different datasets and methods: K-core community discovery method for DBLP dataset and Main Path Analysis method for Arxiv High-energy physics theory citation network. At the end of the analyses, we have obtained several reports that represent the skeleton structure of the communities in the networks.