Communities In Social Networks Research Articles

Evaluating the satisfaction and utility of social networks in medical practice and continuing medical education

BackgroundDigital health has surged during the Covid health crisis, and the use of social media, already prevalent in medicine, has significantly increased. There are Social Networks groups dedicated to physicians with an educational purpose. These groups also facilitate peer discussions on medical questions and the sharing of training materials.ObjectivesThe aim of our study was to assess the value of these new tools and their contribution to medical education.MethodsAn anonymous questionnaire was conducted among members of a Social Networks community group for physicians. The survey received responses from 1451 participants.ResultsThe majority of participants believed they had enriched their medical knowledge and accessed documents they would not have accessed without the group. Subgroup analysis showed that the contribution of this tool is more pronounced for general practitioners and doctors practicing in limited healthcare access.ConclusionIt is essential to develop digital tools that enhance physician training, and social networks represent a valuable educational tool.

The Role of MSMEs in Community Empowerment: A Study in Malang Regency, East Java, Indonesia

This research aims to analyze and describe the impact and contribution of Micro, Small, and Medium Enterprises (MSMEs) in community empowerment. In various developing countries, MSMEs have become the economic backbone of local communities and have a significant influence on improving welfare and strengthening social networks in various communities. This research uses a qualitative descriptive approach, with a study focus on Super Heru MSMEs in Pakis District, Malang Regency, East Java, Indonesia. The research results show that MSMEs have played a crucial role as economic drivers by creating jobs for residents. Its existence can form strong bonds between business actors, consumers, and the community, as well as contribute to local economic growth and increase a sense of community ownership and involvement. MSMEs also play a role in providing access to disadvantaged groups, such as women, youth, and marginalized community groups, by providing business opportunities and skills training. MSMEs have succeeded in empowering many individuals to create their own sources of income, increase economic independence, and reduce social disparities. The conclusion of this research confirms that MSMEs have great potential in strengthening community networks and advancing social welfare. Researchers suggest that with the right support from the government, non-governmental organizations, and society, MSMEs can continue to be agents of positive change in advancing social, economic, and environmental welfare in various communities.

Rural farmers’ perceptions of and adaptations to climate change in Sub-Saharan Africa: Does climate-smart agriculture (CSA) matter in Nigeria and Ethiopia?

The available literature has ignored farmers’ perceptions on the benefits and drawbacks of adopting climate-smart agriculture (CSA) in favor of focusing primarily on profitability and economic constraints. We use the Ethiopian Socioeconomic Survey (ESS) and the General Household Survey from 2018 and 2019 to compare Nigeria and Ethiopia, both of which have sizable rural populations to assess farmers’ climate change perception and their adaptation options in promoting CSA. We first hypothesize that farmers with high tolerance for risk and stable financial resources are more likely to adopt CSA techniques, relying on the adopter perception theory of agricultural innovations and technologies. We address potential selection bias using the Heckman selection model, and estimate our data using multinomial logistic estimator, as well as standard logistic regression for robustness checks. We find that in both Ethiopia and Nigeria, household income and plot size influence farmers’ adaptations to climate change mitigation practices. However, farmers with bigger plots who run the risk of massive production loss tend to adopt measures of coping with climate change. We show that in both Ethiopia and Nigeria, rural farmers’ adaptation decisions are heavily influenced by agricultural extension programs and community social networks. Overall, our work highlights the important role of income, farm size, and climate-related information for investing in climate-smart agricultural methods to curb food insecurity in Sub-Saharan Africa.

Using path coloring of graphs for communication in social networks

An L (p1, p2, p3, … , pm)- labeling of a graph G, has the vertices of G assigned with non-negative integers, such that the vertices at distance j should have at least pj as their label difference. If m = 3 and p1 = 3, p2 = 2, p3 = 1, it is called an L (3, 2, 1)-labeling which is widely studied in the literature. In this paper, we define an L (3, 2, 1)-path coloring of G as a labeling g : V (G) → Z+ such that between every pair of vertices there exists at least one path P where in the labeling restricted to this path is an L (3, 2, 1)-labeling. Among the labels assigned to any vertex of G under g, the maximum label is called the span of g. The L (3, 2, 1)-connection number of a graph G, denoted by k3c (G) is defined as the minimum value of span of g taken over all such labelings g. We call graphs with the special property that k3c (G) = |V (G) | as L (3, 2, 1)-path graceful. In this paper, we obtain k3c (G) of graphs that possess a Hamiltonian path and carry forward the discussion to certain classes of graphs which do not possess a Hamiltonian path, which is novel to this paper. Although different kinds of labeling are studied in the literature with different mathematical constraints imposed, the idea of showing the existence of a graph with a given number as its minimum labeling number has rarely been addressed. We show that given any positive integer, there always exists an L (3, 2, 1)-path graceful graph with the given integer as its k3c (G), thus addressing the inverse question. Finally exploiting the fact that there is no gap on the k3c (G) number line, we give an application of path colorings for secure communication on social networking sites. Efforts to deploy graph coloring in task scheduling, interference-free transmission, etc have been dealt by earlier researchers. In this paper, we deploy the L (3, 2, 1)-path coloring technique defined by us for secure communication in social networks, which has not been dealt with so far.

Finding Subgraphs with Maximum Total Density and Limited Overlap in Weighted Hypergraphs

Finding dense subgraphs in large (hyper)graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs, which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs in weighted hypergraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work, we define and study a natural generalization of the densest subgraph problem in weighted hypergraphs, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise weighted Jaccard coefficient, i.e., the ratio of weights of intersection divided by weights of union on two nodes sets of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large real-world hypergraphs confirms the efficiency and effectiveness of our algorithms.

The impact of microboards on social and community inclusion of adults with intellectual and developmental disabilities: “To not just exist, but to truly live”

Social relationships and community networks have received significant attention for enhancing positive quality of life and inclusion among people with intellectual and developmental disabilities. One community-based initiative that has the potential to promote and strengthen community inclusion of people with intellectual and developmental disabilities is through a MicroboardTM. Utilizing a qualitative descriptive approach, this study explored the experiences of people involved in microboards to learn if and how microboards may influence and strengthen social and community connections. In-depth, semi-structured interviews were conducted with 23 participants involved in microboards in British Columbia, Canada; and data were analysed using thematic analysis. The findings suggest three key roles of microboards including being (i) person-centered, (ii) empowering, and (iii) facilitating collective participation. These key roles and ways to enhance them are important to consider for initiatives designed to strengthen positive social connections and community inclusion outcomes for people with intellectual and developmental disabilities.

남성 노인의 공간이용경험, 사회적 관계와 매체 기반 문화예술 관람 간 관련성

This study aims to examine whether community space experiences and social networks affect media-based cultural activities among older men, using the data from a sample of 989 older men from the 2022 National Culture and Arts Activities Survey. Main findings are as follows; First, older men with community space experiences, especially community center experience, were more likely to attend media-based cultural activities. Second, presence of a spouse, presence of children living together do not have significant effects. But older men with a spouse were less likely to attend media-based culture and arts viewing on cinema. In terms of control variables, older men who were younger or economically inactive were more likely to attend media-based culture and arts viewing. It is found that older man who have used community centers or older adults welfare centers tend to engage in media-based cultural activities. Social relations formed through cultural space experiences play a facilitating role for the older men.

The Terrorist Experiences: Capturing Deradicalization and Counterterrorism in Indonesia

This paper aims to delve into the nuanced and multifaceted realm of terrorism in Indonesia, focusing specifically on understanding the experiences of terrorists themselves, and the subsequent processes of deradicalization and counterterrorism within the country. Through a combination of qualitative research methods, including interviews, case studies, and analysis of extremist narratives, this study seeks to shed light on the motivations, pathways, and challenges associated with terrorism and its aftermath in Indonesia. The research will explore the ideological foundations of terrorism, examining how socio-political, economic, and religious factors contribute to radicalization. It will investigate the tactics used in recruitment and the evolving nature of extremist narratives in Indonesia. Additionally, the paper will assess the effectiveness of various counterterrorism strategies implemented by governmental and non-governmental entities, including law enforcement agencies and civil society organizations. Furthermore, the study will scrutinize the process of deradicalization, rehabilitation, and reintegration of former terrorists, evaluating the success of existing programs and identifying barriers to their implementation. It will also analyze the role of community engagement, religious leaders, and social support networks in preventing radicalization and promoting deradicalization efforts. By synthesizing empirical evidence and insights from Indonesia's context, this paper aims to contribute to the broader discourse on terrorism and counterterrorism strategies. It seeks to provide actionable recommendations for policymakers, practitioners, and researchers to enhance efforts in combating terrorism while fostering peace and resilience in Indonesia and beyond.Terrorism

Fuzzy similarity based hierarchical clustering for communities in twitter social networks

The complexity of the Internet has significantly increased along with its expansions. In particular, social networking has resulted in user clusters pertaining to many communities at different levels. Studies of online communities are a growing area of research user preferences can be categorized including dangerous groups. This work uses pre-processing techniques including stemming, stop words removals, and tokenizations of data using unigram, bigram, and 1–3 g representations. TF-IDFs (Term Frequency-Inverse Document Frequencies) and Word Embeds are used in feature Extractions. Perspectives generate hierarchical data structures which are then combined using consensus matrices followed by computations of dissimilarities between observed sets. This work employs FS-HC (fuzzy similarity based Hierarchical Clustering) to generate dendrograms for views. Finally, consensus matrices are generated by integrating several hierarchical agglomerations using transitive consensus matrix generations. They contain representative data of generated dendrograms. Performance metrics including precisions, recalls, f-measures, accuracies, clustering coefficients, conductance, and contractions are used to evaluate outcomes of benchmarked clustering algorithms. The proposed approach attains higher accuracy value of about 92 % when compared to other existing algorithms. The required data for community discoveries in social media were gathered from Twitter.

Dealing with work overload in the IT industry

PurposeWork overload has become a relevant issue in the Information Technology (IT) industry, with negative effects for individuals and organizations alike. This study aims to analyse the role of personal resources in a broad model regarding the effects of work overload on performance and well-being for the particular case of IT professionals. Considering the specificities of the IT industry, three personal resources were included in this study: one stable personality variable (conscientiousness) and two more malleable variables (work-life balance and psychological detachment).Design/methodology/approachTo test the model, the authors use a sample of 144 IT Portuguese professionals. The authors collected data through an online questionnaire shared in social networks and IT social network communities. The authors use partial least squares (PLS) for data analysis.FindingsThe results show that work overload negatively impacts on employees’ life satisfaction, psychological detachment, work-life balance and task performance. Conscientiousness is positively related with two positive outcomes: task performance and life satisfaction. Work-life balance has a mediating effect in the relationship between work overload and life satisfaction.Practical implicationsThese findings emphasize the need to promote conscientiousness in IT professionals, as well as reduce workload and promote family-friendly working environments to foster work-life balance and life satisfaction.Originality/valueBy testing this model, the authors aim to contribute to the current knowledge on the role of personal resources in the Job Demands-Resources model, which is still unclear and under-researched.

User trust and evaluation mechanism based on community classification calculation in opportunistic social networks

With the development of Internet of Things technology, 5G communication has gradually entered people’s daily lives. The number of network users has also increased dramatically, and it has become the norm for the same user to enjoy the services provided by multiple network service providers and to complete the exchange and sharing of a large amount of information at the same time. However, the existing opportunistic social network routing is not sufficiently scalable in the face of large-scale network data. Moreover, only the transaction information of network users is used as the evaluation evidence, ignoring other information, which may lead to the wrong trust assessment of nodes. Based on this, this study proposes an algorithm called Trust and Evaluation Mechanism for Users Based on Opportunistic Social Network Community Classification Computation (TEMCC). Firstly, communication communities are established based on community classification computation to solve the problem of the explosive growth of network data. Then a trust mechanism based on the Bayesian model is established to identify and judge the trustworthiness of the recommended information between nodes. This approach ensures that more reliable nodes can be selected for interaction and complete data exchange. Through simulation experiments, the delivery rate of this scheme can reach 0.8, and the average end-to-end delay is only 190 ms.

Удосконалення маркетингової діяльності підприємства засобами цифрових комунікацій у соцмережах

Attention in this article is focused on the substantiation of the applied principles of building long-term relationships of interaction between customers and enterprises in the face of modern challenges. The aim of the study is to substantiate and develop recommendations for improving the marketing activities of enterprise by means of digital communications in social networks. To achieve this goal, formal, dialectical and specific methods were used, in particular structural and logical analysis, desk research, content analysis. The article consistently reveals the mechanism of the process of promoting goods/services through digital channels of social networks. The stages of promotion of goods/services through digital channels of social networks are systematized. To carry out monitoring and analytics, it is proposed to use a combined approach, using marketing services and built-in Instagram analytics. The features of the application of the main engagement metrics of the target audience in the Instagram social network are described: ER (engagement rate) and ERR (involvement in the post). Approbation of marketing tools in social media on the example of a manufacturing enterprise has been carried out. The tools for building digital communications in social media are studied. The relationship between personal sales (in salons), sales promotion through an Instagram business account, the launch of paid targeted advertising and the use of influencers or industry experts for cooperation is substantiated. Recommendations for improving marketing activities have been developed. The main directions of improving the marketing activity of enterprise by means of digital communications in social networks are presented. Among them, the following are identified: the closest interaction with their customers through the development of digital communication channels; finding solutions that can affect competitiveness through the formation of a value proposition for a specific product; increasing the recognition of the enterprise’s products in the market, which will allow you to reach a wider audience and gain the trust of consumers.

A NEW POSSIBILITY TO WRTIE ABOUT THE COMMUNITY FOR THE COMMUNITY: THE PRECENCE OF SOCIAL SCIENCE FACULTY MEMBERS IN SOCIAL NETWORKS

The issue of writing should be considered as a historical situation that has made it possible to produce and record knowledge and information. The position of writing is dependent on the tools that enable ideas to transform into the materiality of the writing. Therefore, with the change of writing tools, the possibilities of creating ideas and producing knowledge, along with publishing tools, have also changed. Therefore, the main goal of the article is to clarify the function of and the sharing of writing in the digital situation by the social science faculty members. For the social science professor, the position of writing is the most important and main position of knowledge production for a social scientist. The position of writing about the community for the community in social networks, what new positions has it activated? To answer this question, an in-depth interview was conducted with 6 social scientists who are faculty members, and the data were coded in MAXQD software. Based on the obtained results, the position of writing in social networks has led to rapid writing in the face of the rhythm of the movement of forces shaping the space-time of the current situation and creating an imagination of it. The rapid writing is to generate information and create propositional knowledge about the issues of the present time. The rapid writing is related to the contemporary living characteristic of the social scientist, where knowledge and information flows affect each other.

An improved algorithm for detecting overlapping communities in social network

An improved algorithm for detecting overlapping communities in social network

Text Mining - A Comparative Review of Twitter Sentiments Analysis

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Community Engagement in Smart Cities: A Social Network Analysis and Community Engagement Test

This study, carried out in the ever-changing context of Smart Cities, reveals the complex interactions of digital social networks, digital community involvement, and the Community involvement Test (CET) framework. The examination of data obtained from certain Smart Cities indicates a thriving milieu of community engagement, characterized by a markedly elevated rate of active participation. The identification of urgent issues, particularly those related to environmental sustainability, safety, traffic management, and educational quality, is consistent with the larger urban difficulties that these creative urban centers are facing. Additionally, the CET framework's integration of SNA data and survey results produces very high ratings, indicating the engagement programs' outstanding performance in promoting resident satisfaction and active involvement. These results highlight how digital technologies can help Smart Cities foster vibrant, engaged communities. They also highlight the continued need for smart urban policies to address urgent issues and maintain residents' quality of life while adhering to the Smart City paradigm.

A Survey on Hyperlink Prediction.

As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks and social communication networks to protein-protein interaction networks. In this article, we provide a systematic and comprehensive survey on hyperlink prediction. We adopt a classical taxonomy from link prediction to classify the existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.

Current status of social network use and some related factors among final-year medical students at Hanoi Medical University in the 2022-2023 school year

ABSTRACT Introduction: The study was conducted to describe the current status of social network use and identify some factors related to the use of social networks among final-year medical students at Hanoi Medical University in the 2022 -2023 school year Methods: A cross-sectional descriptive study was conducted on 318 final-year medical students. Results: Facebook is the social network most used by students (96.2%), the lowest is Weibo (1.3%), and 82.4% of students use social networks for communication and exchange purposes. Students use social networks during the day for the most time from 1-2 hours (30.5%). Students who do not live with their families and students majoring in oral and maxillofacial surgery use social networks more during the day. On the contrary, students with average academic performance spent less time using social networks during the day than the other groups. Conclusion: It is necessary to develop further research, conducted on a larger sample size, to have an overall picture of the current status of social network use and its influence on academic performance and other related factors. Keywords: social network, student, medicine, final year, Hanoi Medical University

Automatic identification of false opinions in social networks

This paper presents the problem of detecting false opinions in social networks, also called "opinion spam", describing how lies can be automatically detected using different methods. It has been shown that deception is frequently present in everyday communication in social networks. Deception detection is a well-known challenging problem in any research area, basically because the human ability to detect deception is deficient. Particular studies on social psychology and communications show that the accuracy rates of people's abilities to detect deception are in the range of 55 to 58%, i.e., slightly better than chance. This paper addresses the specific problem of deception detection in communication. Emphasis is placed on those approaches that use affective resources such as categorical and psychometric information provided by natural language processing tools. Finally, we focus on the identification of opinion spam, whose detection is very important for reliable opinion mining. Results obtained using different machine learning methods are presented. The results obtained allow us to see the feasibility of the proposed methodology to carry out the detection of false opinions in social networks by obtaining accuracy values higher than 80%.

Determination of the Relationship Between Genital Self-Image and Sexual Qualtiy of Life in Sexually Active Women

Genital self-image refers to individuals' level of satisfaction with the appearance of their genital organs. In the study, it was aimed to determine the relationship between genital self-image and sexual quality of life in sexually active women. The descriptive and relational screening study was conducted with the participation of 921 sexually active women as an online questionnaire over social communication network between 01 December 2020-01 March 2021. The mean score of the women obtained from the Female Genital Self-Image Scale was found to be 21.35±4.05, and the mean score obtained from the Sexual Quality of Life Scale was determined as 73.90±16.38. A positive and weak relationship was found between the mean score of the women obtained from the Female Genital Self-Image Scale and the mean score obtained from the Sexual Quality of Life Scale (p