Communities In Social Networks Research Articles

Issues of Public Awareness about the Activities of Territorial Public Self-government in Solving Issues of Urban Environment Improvement.

In a modern city, the role of interaction between local governments and the population is increasing. The author considers in detail territorial public self-government (CBT) as an important form of public participation in solving issues of local importance, in particular, landscaping. With the right organization, this institute has a huge potential for involving residents in solving issues of local importance, in particular in the field of urban improvement. The prospects for the development of this institution largely depend on the awareness and involvement of the population in this form of self-government. The low level of public awareness is considered as a factor constraining the development of territorial public self-government. The article presents the results of a survey of residents of the city of Tyumen, which demonstrate a low level of awareness of the population about the activities of CBT. These studies confirmed the need to increase the level of knowledge about the role and tools of CBT among citizens. As a solution to the problem of the existing imbalance between the high potential of CBT and the low level of awareness of the population about its work, the author suggests a number of measures, such as the creation of specialized Internet portals, communities in social networks; the use of social advertising in the media and city events to popularize the activities of CBT. The information base will create an infrastructure for realizing the potential of CBT, will make it possible to identify and implement socially significant initiatives of citizens.

"Alive Despite All the Odds": Сollective Memory of Cossacks in Social Networks

The subject of this research is mnemonic practices, i.e. ways of actualizing the past in the collective memory of modern (2010–2020) Cossacks. I chose Cossack communities in social networks for the analysis because they are part of the public sphere and differ from print or official media in their almost complete absence of censorship and freedom of discussion, even on topics excluded from the official public discourse. The main instruments of my research are content and discourse analysis in the methodology of E. Laclau and S. Mouffe. I have found that the most problematic topics for the collective memory of the revived Cossacks are the genocide of the Cossacks and collaboration during World War II. Two narratives of Cossack history – heroic and victimized - coexist in the heterogeneous Cossack community. Often, both versions of the Cossack past are instrumentalized by the same actor for the political needs of the current moment.

Parameter Tuning of Coronavirus Herd Immunity Optimizer for Detection of Communities in Social Networks

Parameter Tuning of Coronavirus Herd Immunity Optimizer for Detection of Communities in Social Networks

Chuvash Regional Communities in Social Networks

Chuvash Regional Communities in Social Networks

SOCIALIZATION OF A CHILD WITH SPECIAL NEEDS

In the modern world the problem of children inclusion has become very acute. Possibility to communicate is in high demand among children with special needs. Speech barriers can easily be overcome by using social networks and various chat rooms for communication. With virtual communication, shyness and avoidance of communication are reduced for both normal children and children with special needs. The objective of this study is to reveal positive tendency in communication on the Internet among children with special needs. The method of the study is the questionnaire which has been elaborated and adapted for the respondents. The respondents are  teenagers with special needs aged from  to . Main results show that  respondents out of  have not changed their views on communication with their peers. Other  respondents have felt that shyness in communication has been reduced. The remaining  people have had positive interaction experience. They are willing to continue communication in social networks and have found online pen pals. Conclusion. The study has showed that social networking and messaging have a positive impact on the attitude of adolescents with special needs towards communication. Many children with special needs have speech spectrum disorders and have diffi culties in starting informal communication with peers because of shyness and increased time of this communication. However, in virtual communication, these barriers are signifi cantly reduced, the quality of social life, confi dence, overall psychological state are improved. In addition, it is important that respondents “on the other side of the screen” (normal teenagers) have been willing to make contact, showed interest (which might not have happened in everyday life) even if they never had prior communication with people with special needs. Therefore, virtual communication is effective as a way of establishing informal communication for children with special needs.

Communication in Social Networks as a Negative Factor Affecting the Performance of Schoolchildren

This article explores how social media has evolved into an online discussion category where people create content, share it, bookmark it, and chat at incredible speed. With ease of use, speed and reach, social media is rapidly changing public discourse in society and setting trends and agendas in areas ranging from environment and politics to technology and entertainment. Thanks to the invention of social media, people are now sharing ideas, feelings, personal information, photos and videos at an amazing rate. Over the past few years, the increased use of social networking sites has become an international phenomenon. What started as a hobby for some computer literate people has become a social norm and lifestyle for people all over the world. Teens and young adults use these sites as a way to connect with their peers, share information, reinvent their identity, and showcase their social lives. But in fact, this method of communication, as two sides of the coin, brings with it both positive and negative impacts. Social networks are distracting and time-consuming. Schoolchildren sit online for hours, completely ignoring their studies, which leads to a decrease in their academic performance.

Political Communication as Organizational or Actor-Theoretical Finding in The Communication Between Politicians and Citizens in Social-Media

Political communication as participation of citizens has become even broader due the expansion of communication possibilities with digital media and WEB-2.0, self-generated content in social-media on internet has become even more important for a social science analysis. The research results to date have concentrated on quantitative methods that look at the frequency of political communication and focus on the recipients in analysis. However, it is also important to look at the politicians’ side, as a change of perspective makes comparative research in political communication increasingly possible. A qualitative research was conducted with German local politicians in the district of Herford in form of key question interviews under the research question ‘How do politicians reflect on their political communication in social networks with citizens? The objective was to define communication in order to show whether politicians orientate their communication according to actor or organisation-theory. The analysis of the statements shows that politicians base their content very strongly on party guidelines.

Феномен обесценивания людей в социальных сетях

With the advent of social networks, globalization accelerated, ideas of tolerance began to spread, access to information increased, and the speed of communication increased. However, the flip side of this phenomenon is that a person’s feelings are devalued, he is forced to pretend and go into an imaginary world in order to preserve at least some kind of existence, which is now a speculative image on the Internet. The authors conclude that the devaluation of personality in social networks is associated with a person’s unwillingness to perceive other living beings as valuable in themselves and the resulting consumer attitude to the world around them. It is necessary to restructure the architecture of communications in social networks so that they are built on a voluntary response that allows a person to engage in creativity, to realize their own, and not imposed needs.

Community Detection Algorithms in Healthcare Applications: A Systematic Review

Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

Association Between Insurer Connectivity in Appalachian Population Health Networks and Preventable Hospitalizations: Evidence from Kentucky.

Addressing complex health and social needs requires cross-sector collaboration to deliver medical, social, and population health services at the community level. Capacity in community health and social services networks may be constrained in regions like Appalachia due to the combined effects of rurality and persistently poor health and social outcomes. One way that cross-sector networks serving low-resource communities can expand their capacity is by engaging partners, like health insurers, who can leverage resources from outside the local area. This study examines insurer connectivity in cross-sector networks across Kentucky's geographic regions and the association between connectivity and the probability of an individual experiencing a preventable hospitalization. A cross-sectional design was used that linked data from the National Longitudinal Survey of Public Health Systems (NALSYS) with 2018 patient-level Kentucky hospital discharge data to examine the association between insurer connectivity in community networks and preventable hospitalizations across urban, rural non-Appalachian, and Appalachian regions. Analysis of the data shows substantial geographic variation in the association between insurer connectivity in community networks and preventable hospitalization. Insurer connectivity in rural Appalachian communities was associated with lower likelihood that an individual was admitted for a preventable hospitalization ( p < 0.01). Findings suggest insurer connectivity in cross-sector community health and social services networks has the potential to strengthen network capacity to address preventable hospitalizations and improve health outcomes and well-being for the people of Appalachia.

The Influences of Knowledge Sharing, Knowledge Quality, and Relationship Quality on Satisfaction: A Sample of Members on Social Network Communities

The Influences of Knowledge Sharing, Knowledge Quality, and Relationship Quality on Satisfaction: A Sample of Members on Social Network Communities

On Social Scientific Approaches to the Concept of Climate Change

Climate change, both as a natural and a social phenomenon, represents a defining theme and challenge for contemporary global scientific, economic, political and cultural life. In my study, I would like to present an approach to the issue that does not primarily focus on the specific social effects of climate change, but tries to model how the phenomenon is thematised in social communication networks. According to my assumption, as a discursive node that thematizes social knowledge, the concept of climate change also participates in the definition of meaning systems that are much broader than the specific topic. The study presents the concepts of climate change created in social communication processes, reviews the structure and different layers of these concepts, and also the discourses of social communication related to climate change. The study also addresses the problems indicated by the concepts of ‘agency’ and ‘anthropocene’, which, through the interpretation of climate change as a natural and social phenomenon, point to a new thematisation of the relationship between nature and society within the system of social knowledge.

South-South cross-border marriage between Chinese men and Ethiopian women

This article develops an ideal type of South-South cross-border marriage. Based on an 8-month multi-sited ethnography in Ethiopia and China, I identified an unusual conjuncture of global forces, connections, and imaginations that facilitated cross-border marriages between Chinese men and local women in Ethiopia, which should be considered a novel ideal type. Its theoretical novelty is not only defined by the unique dynamics among Sino-Ethiopian spouses vis-à-vis the ‘segregated’ Chinese documented in existing studies but also by these marriages’ distinct formation mechanisms. Sino-Ethiopian marriage is not formed due to China being an attractive destination but associated with China’s incompatible hard and soft power as forces, Chinese factories and accumulated Sino-Ethiopian social networks in local communities as connections, and localized imaginations. Furthermore, this study calls for a paradigm shift in examining cross-border marriages between a developing South and a rising South in this dramatically changing global capitalist world system.

Способы выражения общественного настроения в текстах интернет-сообщества политической направленности социальной сети «ВКонтакте»

The article discusses the most typical ways of expressing public mood that are employed by the participants in discussions in a politically-oriented Internet community. Public mood is understood as a form of mass political consciousness’ existence. The study was conducted on the material of texts posted in the Politika (Politics) community of the VKontakte social network from September 21, 2022 to January 6, 2023. On the basis of the Internet posts and comments to them, the research revealed the preferences of Internet users in choosing the pragmatic type of utterance. The pragmatic type is determined by the predominance of a certain component of public moods (emotional, rational, or rational-emotional). The correspondence between the prevailing type of the Internet users’ reaction and the pragmatic type of utterance was established taking into account the speech act theory developed by J. Austin and J. Searle and the typology of speech acts proposed by N. I. Formanovskaya. The paper points out the predominance of the emotive dominant in the structure of the Russian-speaking Internet users’ public attitudes. It is realized in the employment of mostly rogative, directive, and representative speech acts in the texts under analysis. Other types of speech acts (expressives, contactives) mainly implement the phatic function of communication in the Internet community. The paper concludes that the use of the identified types of speech acts contributes to the expression of subjective experiences of the community members. It also allows the authors of the texts to propose their own scenarios for the development of the political situation under discussion and to influence the formation of attitudes of other Vkontakte users.

Impact of COVID-19 and Corresponding Risk Factors on Gender-Based Violence in Bangladesh

The global scene was drastically changed by the COVID-19 outbreak, causing enormous upheaval in our social, cultural, political, economic and other arenas. Despite their prevalence, pandemics have historically failed to alleviate the plight of gender-based violence. The primary goal of this research was to investigate how the COVID-19 influenced the prevalence of gender-based violence in Bangladesh. Ultimately, the underlying causes and potential hazards contributing to violent behavior were determined. The research utilized a methodology of qualitative content analysis. The COVID-19 outbreak has exacerbated the preexisting problem of gender-based violence in Bangladesh. The study examined various reasons and factors contributing to the issue. These included limitations on mobility, a rise in unemployment rates, decreased social interaction and community networks, financial dependence of women on men, low income and concerns about the future, a shift in priorities towards the medical sector, an increase in domestic workload for women, reduced legal and mental support for women, child marriage, and excessive use of the internet and social media, either through direct or indirect means.

Anomaly Discover: A New Community-based Approach for Detecting Anomalies in Social Networks

In this paper, a new method called Anomaly Discover is provided for detecting anomalies in communities with mixed attributes (binary, numerical and categorical attributes). Our strategy tries to identify unusual users in Online Social Networks (OSN) communities and score them according to how far they deviate from typical users. Our ranking is based on both users’ attributes and network structure. Moreover, for effective anomaly detection, the context-selection process is performed for choosing relevant attributes that demonstrate a strong contrast between normal and abnormal users. So the anomaly score is defined as the degree of divergence in the network structure as well as a context-specific subset of attributes. To assess the efficacy of our model, we used real and artificial networks. We then compared the outcomes to those of two state-of-art models. The outcomes show that our model performs well since it outperforms other models and can pick up anomalies that competing models miss.

Learning Representations by Graphical Mutual Information Estimation and Maximization.

The rich content in various real-world networks such as social networks, biological networks, and communication networks provides unprecedented opportunities for unsupervised machine learning on graphs. This paper investigates the fundamental problem of preserving and extracting abundant information from graph-structured data into embedding space without external supervision. To this end, we generalize conventional mutual information computation from vector space to graph domain and present a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graph and hidden representation. Except for standard GMI which considers graph structures from a local perspective, our further proposed GMI++ additionally captures global topological properties by analyzing the co-occurrence relationship of nodes. GMI and its extension exhibit several benefits: First, they are invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing methods; Second, they can be efficiently estimated and maximized by current mutual information estimation methods; Lastly, our theoretical analysis confirms their correctness and rationality. With the aid of GMI, we develop an unsupervised embedding model and adapt it to the specific anomaly detection task. Extensive experiments indicate that our GMI methods achieve promising performance in various downstream tasks, such as node classification, link prediction, and anomaly detection.

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

In social data analytics, Virtual Community (VC) detection is a primary challenge in discovering user relationships and enhancing social recommendations. VC formation is used for personal interaction between communities. But the usual methods didn’t find the Suspicious Behaviour (SB) needed to make a VC. The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking (GJSBS-RDNNCR) Model addresses these issues. The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks (SN). In the GJSBS-RDNNCR model, the SN is given as an input at the input layer. After that, the User’s Behaviors (UB) are extracted in the first Hidden Layer (HL), and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the SB. In the third HL, the similarity values are examined, and SB tendency is classified using the Activation Function (AF) in the Output Layer (OL). Finally, the ranking process is performed with classified users in SN and their SB. Results analysis is performed with metrics such as Classification Accuracy (CA), Time Complexity (TC), and False Positive Rate (FPR). The experimental setup considers 250 tweet users from the dataset to identify the SBs of users.

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral’s scalability, effective load-balancing, and efficiency in online spam detection for social networks.

Обработка и анализ сообщений для геокодирования аномальных ситуаций

The purpose of the research was to study and implement methods for collecting and processing data obtained from social networks to extract useful information about emergency situations. Information extracted from social media posts can help detect the fact of an emergency and establish its approximate geographical location based on the text of the record reporting the incident. Tools were designed and developed that implement the collection and analysis of records from the VK social network communities. A tool has been created to extract the geographical location described in the analyzed text. Basic natural language processing methods, named entity recognition methods, frequency analysis and geocoding tools are used to extract information. The developed programs may have practical significance for public authorities, security services and other interested organizations in the field of ensuring the safety and protection of the population.