Artykuły w czasopismach na temat „Online community networks”
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Fani, Hossein, i Ebrahim Bagheri. "Community detection in social networks". Encyclopedia with Semantic Computing and Robotic Intelligence 01, nr 01 (marzec 2017): 1630001. http://dx.doi.org/10.1142/s2425038416300019.
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Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.
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Walczak, Steven. "Artificial Neural Network Research in Online Social Networks". International Journal of Virtual Communities and Social Networking 10, nr 4 (październik 2018): 1–15. http://dx.doi.org/10.4018/ijvcsn.2018100101.
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Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.
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Kavanaugh, Andrea, John M. Carroll, Mary Beth Rosson, Than Than Zin i Debbie Denise Reese. "Community Networks: Where Offline Communities Meet Online". Journal of Computer-Mediated Communication 10, nr 4 (lipiec 2005): 00. http://dx.doi.org/10.1111/j.1083-6101.2005.tb00266.x.
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Pan, Gang, Wangsheng Zhang, Zhaohui Wu i Shijian Li. "Online Community Detection for Large Complex Networks". PLoS ONE 9, nr 7 (25.07.2014): e102799. http://dx.doi.org/10.1371/journal.pone.0102799.
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Ai, Chuan, Bin Chen, Hailiang Chen, Weihui Dai i Xiaogang Qiu. "Geographical Structural Features of the WeChat Social Networks". ISPRS International Journal of Geo-Information 9, nr 5 (1.05.2020): 290. http://dx.doi.org/10.3390/ijgi9050290.
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Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.
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Yanchenko, Kostiantyn. "Community, network or both?" Communication & Language at Work 6, nr 2 (23.09.2019): 15–27. http://dx.doi.org/10.7146/claw.v6i2.116081.
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This paper overviews scientific narratives surrounding communities and networks both off- and online and criticizes the dichotomous approach to the topic, according to which each social structure can be classified as either a community or a network. It is argued that such a division does not facilitate comprehension of the contemporary online social structures with their complexity and dynamism. The study provides an alternative view on the issue assuming that community and network are not mutually exclusive concepts and can be studied holistically. The proposed theoretical statement is operationalized and piloted on the example of ‘Aarhus Internationals’ Facebook group – an online venue for international expats in Denmark. A content analysis of the group`s posts showed how exactly community and network aspects of social structures may coexist and interact online.
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Nova, Fayika Farhat, Amanda Coupe, Elizabeth D. Mynatt, Shion Guha i Jessica A. Pater. "Cultivating the Community". Proceedings of the ACM on Human-Computer Interaction 6, GROUP (14.01.2022): 1–33. http://dx.doi.org/10.1145/3492826.
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A growing body of HCI research has sought to understand how online networks are utilized in the adoption and maintenance of disordered activities and behaviors associated with mental illness, including eating habits. However, individual-level influences over discrete online eating disorder (ED) communities are not yet well understood. This study reports results from a comprehensive network and content analysis (combining computational topic modeling and qualitative thematic analysis) of over 32,000 public tweets collected using popular ED-related hashtags during May 2020. Our findings indicate that this ED network in Twitter consists of multiple smaller ED communities where a majority of the nodes are exposed to unhealthy ED contents through retweeting certain influential central nodes. The emergence of novel linguistic indicators and trends (e.g., "#meanspo") also demonstrates the evolving nature of the ED network. This paper contextualizes ED influence in online communities through node-level participation and engagement, as well as relates emerging ED contents with established online behaviors, such as self-harassment.
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Yin, Naian, Yachao Lu i Nan Zhang. "Speed up random walk by leveraging community affiliation information". CCF Transactions on Pervasive Computing and Interaction 2, nr 1 (13.11.2019): 51–65. http://dx.doi.org/10.1007/s42486-019-00021-2.
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Abstract Large online networks are most massive and opulent data sources these days. The inherent growing demands of analyses related data fetching conflict greatly with network providers’ efforts to protect their digital assets as well as users’ increasing awareness of privacy. Restrictions on web interfaces of online networks prevent third party researchers from gathering sufficient data and further global images of these networks are also hidden. Under such circumstances, only techniques like random walk approaches that can run under local neighborhood access will be adopted to fulfill large online network sampling tasks. Meanwhile, the presence of highly clustered community like structure in large networks leads to random walk’s poor conductance, causing intolerable and hard-to-foresee long mixing time before useful samples can be collected. With lack of techniques incorporate online network topology features being the context, in this paper we focus on taking use of community affiliation information that possibly comes with metadata when querying objects in online networks, and proposed a speeded version of random walk by raising the probability of inter-community edges being selected. Assuming the community structure is well established as promised, the community speeded random walk expects better conductance and faster convergence. Our method forces the sampler to travel rapidly among different communities that conquers the bottlenecks and thus the samples being collected are of higher quality. We also consider the scenario when community affiliation is not directly available, where we apply feature selection algorithms to select features as community.
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Dhumal, Amit, i Pravin Kamde. "Survey on Community Detection in Online Social Networks". International Journal of Computer Applications 121, nr 9 (18.07.2015): 35–41. http://dx.doi.org/10.5120/21571-4609.
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Zhang, Xuewu, Huangbin You, William Zhu, Shaojie Qiao, Jianwu Li, Louis Alberto Gutierrez, Zhuo Zhang i Xinnan Fan. "Overlapping community identification approach in online social networks". Physica A: Statistical Mechanics and its Applications 421 (marzec 2015): 233–48. http://dx.doi.org/10.1016/j.physa.2014.10.095.
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Zheng, Xu, Zhipeng Cai, Guangchun Luo, Ling Tian i Xiao Bai. "Privacy-preserved community discovery in online social networks". Future Generation Computer Systems 93 (kwiecień 2019): 1002–9. http://dx.doi.org/10.1016/j.future.2018.04.020.
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Ge, Jun, Lei-lei Shi, Lu Liu, Hongwei Shi i John Panneerselvam. "Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks". Wireless Communications and Mobile Computing 2021 (31.05.2021): 1–13. http://dx.doi.org/10.1155/2021/3860083.
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Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.
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Singh, Bikash Chandra, Mohammad Muntasir Rahman, Md Sipon Miah i Mrinal Kanti Baowaly. "Community Detection Using Node Attributes and Structural Patterns in Online Social Networks". Computer and Information Science 10, nr 4 (31.10.2017): 50. http://dx.doi.org/10.5539/cis.v10n4p50.
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Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.
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Song, Xiaolong, Jiahua Jin, Yi-Hung Liu i Xiangbin Yan. "Lose your weight with online buddies: behavioral contagion in an online weight-loss community". Information Technology & People 33, nr 1 (29.03.2019): 22–36. http://dx.doi.org/10.1108/itp-11-2018-0525.
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Purpose A question of interest is whether online social networks are effective in promoting behavioral changes and weight loss. The purpose of this paper is to examine the contagion effect of an online buddy network on individuals’ self-monitoring behavior. Design/methodology/approach This study collects data from an online weight-loss community and constructs an online buddy network. This study compares the effects of the network structure of the buddy network and the actor attributes when predicting self-monitoring performance by employing the auto-logistic actor attribute models. Findings This study confirms the contagion effect on weigh-in behavior in the online buddy network. The contagion effect is significantly predictive when controlling for actor attribute and other network structure effects. Originality/value There is limited evidence that one’s weight-related behavior can be affected by online social contacts. This study contributes to the literature on peer influence on health by examining the contagion effect on weight-related behavior between online buddies. The findings can assist in designing peer-based interventions to harness influence from online social contacts for weight loss.
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Sanchez, Joje Mar P., Blanca A. Alejandro, Michelle Mae J. Olvido i Isidro Max V. Alejandro. "An Analysis of Online Classes Tweets Using Gephi: Inputs for Online Learning". International Journal of Information and Education Technology 11, nr 12 (2021): 583–89. http://dx.doi.org/10.18178/ijiet.2021.11.12.1568.
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The conduct of online classes has emerged as one of the major changes in the educational landscape at the onset of COVID-19. Its implementation has been met by varying reactions that have become evident in social media, particularly on Twitter. This paper analyzed #onlineclasses tweets of Filipino users using network analysis through Gephi and NodeXL software. The resulting network has 2,278 users and 998 interactions with many groups of small interactions among users, and low clustering coefficient and modularity values. The users in the top 8 communities in the network talk about the challenges brought about by online classes and the opportunities that online networks offer. Hence, the network of #OnlineClasses tweets can be described as a community cluster. Smaller groups of users who engaged in aspects of online classes emerge in the network, signifying that Filipinos have differing points of view about the topic. Sentiment sharing through social networks provides an avenue for sharing challenges and building communities that help address challenges for online learning in the pandemic.
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Nazi, Azade, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang i Gautam Das. "Answering Complex Queries in an Online Community Network". Proceedings of the International AAAI Conference on Web and Social Media 9, nr 1 (3.08.2021): 662–65. http://dx.doi.org/10.1609/icwsm.v9i1.14671.
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An online community network such as Twitter or amazon.com links entities (e.g., users, products) with various relationships (e.g., friendship, co-purchase) and make such information available for access through a web interface. The web interfaces of these networks often support features such as keyword search and "get-neighbors" — so a visitor can quickly find entities (e.g., users/products) of interest. Nonetheless, the interface is usually too restrictive to answer complex queries such as (1) find 100 Twitter users from California with at least 100 followers who talked about ICWSM last year or (2) find 100 books with at least 200 5-star reviews at amazon.com. In this paper, we introduce the novel problem of answering complex queries that involve non-searchable attributes through the web interface of an online community network. We model such a network as a heterogeneous graph with two access channels, Content Search and Local Search. We propose a unified approach that transforms the complex query into a small number of supported ones based on a strategic query-selection process. We conduct comprehensive experiments on Twitter and amazon.com which demonstrate the efficacy of our proposed algorithms.
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Hammedi, Wafa, Jay Kandampully, Ting Ting (Christina) Zhang i Lucille Bouquiaux. "Online customer engagement". Journal of Service Management 26, nr 5 (19.10.2015): 777–806. http://dx.doi.org/10.1108/josm-11-2014-0295.
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Purpose – The emergence and success of online brand communities in the marketplace have attracted considerable interest; this study seeks to determine the conditions in which people create social environments by investigating the drivers of connections to a focal online brand community and other brand communities. The purpose of this paper is to investigate the composition of multi-community networks, focussing on the density and centrality of brand communities. Design/methodology/approach – On the basis of insights from prior literature, the proposed model examines customers’ social relationships with multiple brand communities. A survey of 290 participants spans eight brand communities. The modeling process used structural equation modeling; the analysis of the social relationship among brand communities relied on an ego network approach. Findings – Two drivers prompt connections to other online brand communities. First, personal identification with a core brand community enhances connections to other communities. Second, some core brand members choose a functionality-driven approach in creating social environments. Practical implications – For marketers, this study highlights the importance of positioning the brand community as part of a social environment. To strengthen customer-brand relationships, marketers should focus on community members’ multiple memberships. Originality/value – This paper extends understanding of online brand community members’ motivations to participate in a focal brand community. It also explains the creation of a social environment, through a careful consideration of participation in different brand communities and their relationships.
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Liu, Chuchu, i Xin Lu. "Network Evolution of a Large Online MSM Dating Community: 2005–2018". International Journal of Environmental Research and Public Health 16, nr 22 (6.11.2019): 4322. http://dx.doi.org/10.3390/ijerph16224322.
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Due to multiple sexual partners and low rates of condom use, the HIV infection rate among MSM (men who have sex with men) is much higher than that of the general population. In order to analyze the characteristics of online activities of MSM, and to understand the evolution of their social networks, in this study we collect a comprehensive dataset, covering the period from January 2005 to June 2018, from the largest Chinese online community, Baidu Tieba. We build an online dating network for MSM-related individuals in the gay-bar community, and analyze the network from static and dynamic aspects. It is found that there is a strong homophily regarding the cities where users reside when developing interactions with others, and that most network measurements tend to be stable at the later stages of evolution, while the size of the largest community fluctuates. This is an indication that the network is formed of rapidly flexible interactions which changes quickly. In comparison with studies on heterosexual networks, we find that the MSM dating network shows differences in many aspects, such as the positive degree-degree correlation and high clustering coefficient, suggesting different thinking and measures should be taken in the policy making of public health management towards the MSM population.
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Huang, Jianbin, Heli Sun, Yaguang Liu, Qinbao Song i Tim Weninger. "Towards Online Multiresolution Community Detection in Large-Scale Networks". PLoS ONE 6, nr 8 (24.08.2011): e23829. http://dx.doi.org/10.1371/journal.pone.0023829.
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Newman, Ken. "Narrative in an online community". International Journal of Web Based Communities 1, nr 4 (2005): 475. http://dx.doi.org/10.1504/ijwbc.2005.008112.
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Wadhwa, Pooja, i M. P. S. Bhatia. "Community Detection Approaches in Real World Networks". International Journal of Virtual Communities and Social Networking 6, nr 1 (styczeń 2014): 35–51. http://dx.doi.org/10.4018/ijvcsn.2014010103.
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Online social networks have been continuously evolving and one of their prominent features is the evolution of communities which can be characterized as a group of people who share a common relationship among themselves. Earlier studies on social network analysis focused on static network structures rather than dynamic processes, however, with the passage of time, the networks have also evolved and the researchers have started to focus on the aspect of studying dynamic behavior of networks. This paper aims to present an overview of community detection approaches graduating from static community detection methods towards the methods to identify dynamic communities in networks. The authors also present a classification of the existing dynamic community detection algorithms along the dimension of studying the evolution as either a two-step approach comprising of community detection via static methods and then applying temporal dynamics or a unified approach which comprises of dynamic detection of communities along with their evolutionary characteristics.
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Selvakumar, M., i A. Vijaya Kathiravan. "Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 8s (18.08.2023): 202–14. http://dx.doi.org/10.17762/ijritcc.v11i8s.7191.
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Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities. To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods.
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Lin, Hui, i Shijuan Li. "Analysis of User Social Support Network in Online Tumor Community". Data and Information Management 5, nr 1 (6.11.2020): 184–94. http://dx.doi.org/10.2478/dim-2020-0040.
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AbstractWith the development of Internet technology, online health forums have become indispensable for people who seek non-professional health support. This research focuses on the content posted by cancer patients and their relatives in online health forums and social networks to raise the following research questions: What is the overall view of the social support network in the online tumor community? What are the information behaviors of the online tumor community in different identities of users? How users interact in this community and build this network of social support? What are the topics users would like to share and talk about? What kinds of users could be the key users in this community? Method: Using the post and comment data of the Oncology Forum of Tianya Hospital in 2019, combined with social network analysis and word co-occurrence network analysis, the following conclusions are obtained: (1) There are some central points in the overall social support network, and there are central users consistent with other social networks. (2) Positive users are more likely to comment on others, and it is easier to get others’ comments, while negative users are more likely to share personal information and do not want to participate more in social interaction. (3) Users focus on posting emotional and emotional content in content sharing. Information-based social support information. The social support experience that this type of information brings to users can be positive and negative. (4) The most active group in the patients’ online health community, followed by the patients’ children. (5) The relationship between users and patients is diverse and there are two types of singularity. Users with diverse relationships are more likely to be commented on, and they are more willing to comment on users who also have diverse relationships.
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Årsand, E., L. Fernandez-Luque, J. Lauritzen, G. Hartvigsen i T. Chomutare. "Inferring Community Structure in Healthcare Forums". Methods of Information in Medicine 52, nr 02 (2013): 160–67. http://dx.doi.org/10.3414/me12-02-0003.
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SummaryBackground: Detecting community structures in complex networks is a problem interesting to several domains. In healthcare, discovering communities may enhance the quality of web offerings for people with chronic diseases. Understanding the social dynamics and community attachments is key to predicting and influencing interaction and information flow to the right patients.Objectives: The goal of the study is to empirically assess the extent to which we can infer meaningful community structures from implicit networks of peer interaction in online healthcare forums.Methods: We used datasets from five online diabetes forums to design networks based on peer-interactions. A quality function based on user interaction similarity was used to assess the quality of the discovered communities to complement existing homophily measures.Results: Results show that we can infer meaningful communities by observing forum interactions. Closely similar users tended to co-appear in the top communities, suggesting the discovered communities are intuitive. The number of years since diagnosis was a significant factor for cohesiveness in some diabetes communities.Conclusion: Network analysis is a tool that can be useful in studying implicit networks that form in healthcare forums. Current analysis informs further work on predicting and influencing interaction, information flow and user interests that could be useful for personalizing medical social media.
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Stellefson, Michael, Samantha R. Paige, Julia M. Alber i Margaret Stewart. "COPD360social Online Community: A Social Media Review". Health Promotion Practice 19, nr 4 (8.06.2018): 489–91. http://dx.doi.org/10.1177/1524839918779567.
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People living with chronic obstructive pulmonary disease (COPD) commonly report feelings of loneliness and social isolation due to lack of support from family, friends, and health care providers. COPD360social is an interactive and disease-specific online community and social network dedicated to connecting people living with COPD to evidence-based resources. Through free access to collaborative forums, members can explore, engage, and discuss an array of disease-related topics, such as symptom management. This social media review provides an overview of COPD360social, specifically its features that practitioners can leverage to facilitate patient–provider communication, knowledge translation, and community building. The potential of COPD360social for chronic disease self-management is maximized through community recognition programming and interactive friend-finding tools that encourage members to share their own stories through blogs and multimedia (e.g., images, videos). The platform also fosters collaborative knowledge dissemination and helping relationships among patients, family members, friends, and health care providers. Successful implementation of COPD360social has dramatically expanded patient education and self-management support resources for people affected by COPD. Practitioners should refer patients and their families to online social networks such as COPD360social to increase knowledge and awareness of evidence-based chronic disease management practices.
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Abulaish, Muhammad, i Sajid Y. Bhat. "Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks". Foundations of Computing and Decision Sciences 40, nr 2 (1.06.2015): 89–105. http://dx.doi.org/10.1515/fcds-2015-0006.
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Abstract As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users’ interaction network and uses popular classifier ensembles – bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.
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Robaeyst, Ben, Bastiaan Baccarne, Jonas De Meulenaere i Peter Mechant. "Online Neighborhood Networks: The Relationship Between Online Communication Practices and Neighborhood Dynamics". Media and Communication 10, nr 2 (26.05.2022): 108–18. http://dx.doi.org/10.17645/mac.v10i2.5129.
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This article builds upon communication infrastructure theory and investigates how communication practices on online neighborhood networks (ONNs) relate to the social cohesion of neighborhood communities. Specifically, we study the hyperlocal social media platform Hoplr, which provides ad-free ONNs in which neighbors can communicate with one another. Local governments can subscribe to Hoplr to communicate with their residents and engage them for community and public participation purposes. This study is based on an online survey of Hoplr members (N = 3,055) from 150 randomly selected ONNs. Social cohesion is disentangled as a combination of social support, a sense of community, reciprocal exchange, and social trust. We investigated social cohesion differences at the neighborhood level in relation to self-reported types of ONN communication practices (shared interest, supportive communication, and both tangible and informational support mobilization). The results reveal the limited value of quantified behavioral data to explain differences in neighborhood social cohesion. However, interesting patterns are revealed between different communication practices and neighborhood social cohesion, such as the importance of trivial storytelling and information exchange practices for enhancing trust, reciprocal support, and a sense of community. At the same time, a reversed relation appears when ONNs are considered explicit information exchange platforms. With these insights, we enhance the theoretical understanding of ONNs in relation to neighborhood social cohesion and within a broader repertoire of neighborhood communication infrastructures.
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Rahim, Nurul Zahirah Abd, Nurun Najwa Bahari, Nur Syaza Mohd Azzimi, Zamira Hasanah Zamzuri, Hafizah Bahaludin, Nurul Farahain Mohammad i Fatimah Abdul Razak. "Comparing Friends and Peer Tutors Amidst COVID-19 Using Social Network Analysis". Mathematics 11, nr 4 (20.02.2023): 1053. http://dx.doi.org/10.3390/math11041053.
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COVID-19 has drastically changed the teaching patterns of higher education from face-to-face to online learning, and it has also affected students’ engagement socially and academically. Understanding the nature of students’ engagement during online learning can help in identifying related issues so that various initiatives can be implemented in adapting to this situation. In this study, social network analysis is conducted to gain insights on students’ engagement during COVID-19. Directed and weighted networks were used to visualize and analyze friendship as well as peer tutor networks obtained from online questionnaires answered by all students in the class. Contrasting friends and peer tutors reveals some hidden interactions between students and shines some light on dynamics of the online learning community. The results indicate that, popular and important peer tutors may not be high achievers and thus possibly contributing to the spread of misinformation in the online learning community. By comparing weighted indegree and betweenness centrality values, we suggest approaches to cultivate a healthy online learning community. This study highlights the use of social network analysis to assist and monitor students’ engagement and further formulate strategies in order to make the class a conducive online learning community, particularly in the advent of online learning in higher education institutions.
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Berry, Sharla. "Student Support Networks in Online Doctoral Programs: Exploring Nested Communities". International Journal of Doctoral Studies 12 (2017): 033–48. http://dx.doi.org/10.28945/3676.
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Aim/Purpose: Enrollment in online doctoral programs has grown over the past decade. A sense of community, defined as feelings of closeness within a social group, is vital to retention, but few studies have explored how online doctoral students create community. Background: In this qualitative case study, I explore how students in one online doctoral program created a learning community. Methodology: Data for the study was drawn from 60 hours of video footage from six online courses, the message boards from the six courses, and twenty interviews with first and second-year students. Contribution: Findings from this study indicate that the structure of the social network in an online doctoral program is significantly different from the structure of learning communities in face-to-face programs. In the online program, the doctoral community was more insular, more peer-centered, and less reliant on faculty support than in in-person programs. Findings: Utilizing a nested communities theoretical framework, I identified four subgroups that informed online doctoral students’ sense of community: cohort, class groups, small peer groups, and study groups. Students interacted frequently with members of each of the aforementioned social groups and drew academic, social, and emotional support from their interactions. Recommendations for Practitioners: Data from this study suggests that online doctoral students are interested in making social and academic connections. Practitioners should leverage technology and on-campus supports to promote extracurricular interactions for online students. Recommendation for Researchers: Rather than focus on professional socialization, students in the online doctoral community were interested in providing social and academic support to peers. Researchers should consider how socialization in online doctoral programs differs from traditional, face-to-face programs. Impact on Society: As universities increase online offerings, it is important to consider the issues that impact retention in online programs. By identifying the social structures that support online community, this study helps build knowledge around retention and engagement of online students. Future Research: Future research should continue to explore the unique social networks that support online students.
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Ho, Thanh, i Phuc Do. "A New Model for Discovering Communities of Users on Social Network". Science and Technology Development Journal 19, nr 1 (31.03.2016): 81–94. http://dx.doi.org/10.32508/stdj.v19i1.613.
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The trend of technological development and increasing varieties of social media lead to the changes in people’s behaviors in society and forming online communities. Changes of human’s behaviors make many models of business, marketing, services and even the field of education, security, politicsl change from approaches to user management. Community of users on social networks influence behaviors, habits of each user involved in the community. Therefore, exploring community on social networks from many different data sources via analyzing exchanged contents will help know the user community’s behaviors which are reflected in the content and topics that users are interested in discussing in messages. In this paper, we propose a new model of discovering communities of users on social networks based on the topic model combined with Kohonen network. In the proposed model, we focus on discovering communities of users on social networks and analyzing the interested topics change of online community in each period of time. The proposed model is experimented with a set of vectors in interested topics of online users in higher education field.
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Lu, Yingjie, Xinwei Wang, Lin Su i Han Zhao. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities". Mathematics 11, nr 21 (24.10.2023): 4412. http://dx.doi.org/10.3390/math11214412.
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Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.
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SHARMA, SANJIV, i G. N. PUROHIT. "A NOVEL FRAMEWORK FOR TRACKING ONLINE COMMUNITY INTERACTION IN SOCIAL NETWORK". International Journal of Information Acquisition 09, nr 02 (czerwiec 2013): 1350011. http://dx.doi.org/10.1142/s0219878913500113.
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This paper focuses on a design of improved framework and analysis of existing framework which exploits certain algorithms for tracking online community in social network. Tracking of online community is an imperative task where the goal is to identify meaningful group structures in the dynamic social network and consider the problem of the evolution of groups of users in dynamic scenarios. Existing frameworks for tracking community in social network have some limitation which makes it less scalable and computationally inefficient. This novel framework facilitates scalable tracking communities over the time in social networks and offers efficient methods to deal with the problems which are offered in most of the existing frameworks.
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Nugent, Elizabeth R., i Chantal E. Berman. "Ctrl-Alt-Revolt?" Middle East Law and Governance 10, nr 1 (28.03.2018): 59–90. http://dx.doi.org/10.1163/18763375-01001007.
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Analyses of the 2011 Egyptian uprising assign a significant mobilizing role to the interpersonal networks created through Facebook and Twitter. However, these studies fail to investigate online networks in comparison with more traditional “offline” networks, which are similarly theorized to mobilize members to protest participation. In this paper, we analyze nationally representative Arab Barometer survey data from Egypt 2011 to compare the mobilizing effects of memberships in four different types of networks: online, union, community, and religious. We test whether these networks were distinct and operated in competition, or overlapping and operated in tandem to mobilize Egyptians to protest. We demonstrate that different networks mobilized different segments of the population, consistent with theories about the negative revolutionary coalition necessary for successful uprisings. We also show that multiple network membership increases protest propensity, and that individuals at the intersection of online networks and community group networks, such as those formed through membership in charity groups or sports clubs, are most likely to engage in revolutionary protest. These results speak to an important interactive effect between online and offline networks in terms of facilitating successful revolutionary uprisings.
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Wang, Xuan, Bofeng Zhang i Furong Chang. "Hot Topic Community Discovery on Cross Social Networks". Future Internet 11, nr 3 (4.03.2019): 60. http://dx.doi.org/10.3390/fi11030060.
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The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.
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Su, Chang, Xiaohong Guan, Youtian Du, Qian Wang i Fei Wang. "A fast multi-level algorithm for community detection in directed online social networks". Journal of Information Science 44, nr 3 (14.03.2017): 392–407. http://dx.doi.org/10.1177/0165551517698305.
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The discovery of underlying community structures plays a significant role in online social network (OSN) analysis. Many previous methods suffer from inaccuracy or incompleteness in community descriptions because of the multiple factors affecting OSNs and the high computational complexity caused by the large scale of these networks. We present a new community detection approach that focuses on two aspects. First, it relies on a combination of user interests and cohesiveness in describing community structures. Second, it introduces a multi-level community discovery algorithm for large-scale OSN datasets. The algorithm consists of three steps: (1) network coarsening based on the combination of two categories of properties, (2) stochastic inference to find an initial community assignment over the coarsest network and (3) projection and refinement of this assignment to obtain the final community detection result by solving a semi-supervised learning problem. The combination of user interests and cohesiveness leads to a complete and well-interpreted description of the communities embedded in OSNs, and the multi-level algorithm speeds up the computation process and improves the likelihood of finding the global optimal solution by reducing the parameter space. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness and efficiency of our method.
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Rizos, Georgios, Symeon Papadopoulos i Yiannis Kompatsiaris. "Multilabel user classification using the community structure of online networks". PLOS ONE 12, nr 3 (9.03.2017): e0173347. http://dx.doi.org/10.1371/journal.pone.0173347.
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Fan, W., i K. H. Yeung. "Incorporating profile information in community detection for online social networks". Physica A: Statistical Mechanics and its Applications 405 (lipiec 2014): 226–34. http://dx.doi.org/10.1016/j.physa.2014.02.075.
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Guidi, Barbara, Andrea Michienzi i Giulio Rossetti. "Towards the Dynamic Community Discovery in Decentralized Online Social Networks". Journal of Grid Computing 17, nr 1 (12.07.2018): 23–44. http://dx.doi.org/10.1007/s10723-018-9448-0.
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Sundaram, Hari, Yu-Ru Lin, Munmun De Choudhury i Aisling Kelliher. "Understanding Community Dynamics in Online Social Networks: A multidisciplinary review". IEEE Signal Processing Magazine 29, nr 2 (marzec 2012): 33–40. http://dx.doi.org/10.1109/msp.2011.943583.
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Traud, Amanda L., Eric D. Kelsic, Peter J. Mucha i Mason A. Porter. "Comparing Community Structure to Characteristics in Online Collegiate Social Networks". SIAM Review 53, nr 3 (styczeń 2011): 526–43. http://dx.doi.org/10.1137/080734315.
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Asim, Yousra, Ahmad Kamran Malik, Basit Raza, Wajeeha Naeem i Saima Rathore. "Community-Centric Brokerage-Aware Access Control for Online Social Networks". Future Generation Computer Systems 109 (sierpień 2020): 469–78. http://dx.doi.org/10.1016/j.future.2018.08.023.
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Nair, Vasanth, i Sumeet Dua. "Folksonomy-based ad hoc community detection in online social networks". Social Network Analysis and Mining 2, nr 4 (23.08.2012): 305–28. http://dx.doi.org/10.1007/s13278-012-0081-9.
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Xia, Haoxiang. "A Collective-Intelligence View on the Linux Kernel Developer Community". International Journal of Knowledge and Systems Science 1, nr 3 (lipiec 2010): 20–32. http://dx.doi.org/10.4018/jkss.2010070102.
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With the rapid proliferation of all sorts of online communities, the knowledge creation and dissemination in these online communities have become a prominent social phenomenon. In this paper, one typical Open Source Software community—the online community of Linux kernel developers—is studied from the perspective of collective intelligence, to explore the social dynamics behind the success of the Linux kernel project. The Linux kernel developer community is modeled as a supernetwork of triple interwoven networks, namely a technological media network, a collaboration network of the developers, and a knowledge network. The development of the LDC is then an evolutionary process through which the supernetwork expands and the collective intelligence of the community develops. In this paper, a bottom-up approach is attempted to unravel this evolutionary process.
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Fen Crystal Yap, Sheau, i Christina Kwai Choi Lee. "Leveraging the power of online social networks: a contingency approach". Marketing Intelligence & Planning 32, nr 3 (29.04.2014): 345–74. http://dx.doi.org/10.1108/mip-03-2013-0048.
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Purpose – The purpose of this paper is to examine online community loyalty through an extension of the theory of planned behaviour by incorporating motivational drivers of enjoyment and compatibility and the moderating role of consumer traits. Design/methodology/approach – Data were collected using an online survey of 382 Facebook users in Malaysia. Structural equation modelling was used to assess the hypothesised relationships. Findings – Findings reveal that individuals’ attitude towards social network usage is associated with three factors: social influence, compatibility and enjoyment; attitude and usage behaviour are the determinants of online community loyalty. In addition, moderating effects are found in innovativeness and social network user experience. Research limitations/implications – Generalisation of the results to other contexts or populations should be made with caution given the study's focus on Facebook and its use of non-probability sampling. Future research can cross-validate or extend the theoretical model across different samples and/or virtual community settings. Practical implications – This study highlights the importance of designing online brand community web sites which are not only appealing and enjoyable but also acts as an outlet for its members to build upon their experiences and showcase their innovativeness. Originality/value – This research contributes to a better understanding of how personal factors can either strengthen or attenuate a member's loyalty to his or her online community. The research framework developed in this study can serve as a springboard for future research to examine other virtual community engagement such as blogging, online advertising and online public relation activities.
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Vaghefi, Mahyar Sharif, i Derek L. Nazareth. "Mining Online Social Networks: Deriving User Preferences through Node Embedding". Journal of the Association for Information Systems 22, nr 6 (2021): 1625–58. http://dx.doi.org/10.17705/1jais.00711.
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In the last decade, online social networks have become an integral part of life. These networks play an important role in the dissemination of news, individual communication, disclosure of information, and business operations. Understanding the structure and implications of these networks is of great interest to both academia and industry. However, the unstructured nature of the graphs and the complexity of existing network analysis methods limit the effective analysis of these networks, particularly on a large scale. In this research, we propose a simple but effective node embedding method for the analysis of graphs with a focus on its application in online social networks. Our proposed method not only quantifies social graphs in a structured format but also enables user preference identification, community detection, and link prediction in online social networks. We demonstrate the effectiveness of our approach using a network of Twitter users. The results of this research provide valuable insights for marketing professionals seeking to target personalized content and advertising to individual users as well as social network administrators seeking to improve their platform through recommendation systems and the detection of outliers and anomalies.
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Cooray, Shavindrie, i Steven Gunning. "Analysing Online Social Networks from a Soft Systems Perspective". International Journal of Systems and Society 3, nr 2 (lipiec 2016): 1–20. http://dx.doi.org/10.4018/ijss.2016070101.
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In recent years online communities have become popular as a way for dispersed members to interact and share ideas. However, there is limited research on the systemic analysis of such groups. In this paper the authors draw on systems research to suggest that one can only understand or interpret data gathered from an online group by assigning meaning to it in the context of a wider ‘system'. Here they demonstrate that conventional methods of offline group analysis are ineffective when applied within online communities. As an alternative they offer a means of importing big data from a community on social media and identifying the kinds of information that would be typically gathered during an offline systemic analysis into a problem situation. The authors offer as an aid to managers a framework that identifies appropriate Social Network Analysis (SNA) metrics (from the imported data) that corresponds to soft systems concepts. During their analysis of a community on Twitter they found that their ideas provided a way to gain a more holistic understanding of the roles and interactions in a virtual community. The authors also found examples of the use of informal power in the Twitter community under study. This information can be useful to managers when producing marketing plans, during product development and when identifying opportunities for growth.
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Sukma, Narongsak, i Adisorn Leelasantitham. "Understanding online behavior towards community water user participation: A perspective of a developing country". PLOS ONE 17, nr 7 (28.07.2022): e0270137. http://dx.doi.org/10.1371/journal.pone.0270137.
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The social network is a network of virtual relationships that can facilitate the development of a new society in which everyone can use online communication effectively. This article investigates and identifies the fundamental influences on the social network system, as well as the online behavior of the community users. This study was designed by any social network to help improve efficiency and offer people with services that match the needs of their communities. Furthermore, it increases participation in the equitable distribution of social benefits. This study investigates the critical factors that impact a community’s view of community water user participation. The researcher sent a questionnaire on a five-point Likert scale to 1,000 community water customers and collected 627 valid replies. Data from 14 villages were sampled using a simple random sampling strategy to acquire the data. Subsequently, descriptive statistics are used to describe the data (frequency distributions, percentages, averages, medians, and standard deviation). Furthermore, PLS-SEM was used to examine the relationships between factors and to launch the conceptual model using PLS route modeling. This study reveals that digital technologies are crucial to increasing the expectations and happiness of the community through social networks. Multiple causes contribute to its expansion. In addition, this research provides an outstanding case study technique based on TAM and ECT to assess people’s social networking and community participation habits. Additionally, community water providers participate in social networks by certifying that their expectations are met.
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Mendoza Mazo, Edwin Alberto, Wilson Javier Guerra Peinado i Giovanny Mancilla Gaona. "Digital networks community experiences: case Buenos Aires libre". Visión electrónica 11, nr 2 (27.10.2018): 211–21. http://dx.doi.org/10.14483/22484728.14629.
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Community Digital Networks (CDN) are understood as those networks type LAN that allow free wireless (using wireless technology defined by the family of standards for wireless IEEE 802.11) to different types of resources and services (available online or in a local network) those are characterized by being designed and implemented by groups or non-profit organizations, and effectively contribute to improving the quality of life of the communities where they work -whose digital divide, in general, it is meaningful-. This article reviews some experiences of CDN in Europe, Asia and South America, in the period 2008-2016; as a case study and it is specified in the Buenos Aires Libre network, established in the province of Buenos Aires (Argentina). The research has a prospective profile and set out the shortest way to overcome the technological marginalization that afflicts vulnerable communities in Latin America, with Colombia in the head, through free access to the information by electronic media.
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Kim, Sung-Hwan, i Hwan-Gue Cho. "User–Topic Modeling for Online Community Analysis". Applied Sciences 10, nr 10 (14.05.2020): 3388. http://dx.doi.org/10.3390/app10103388.
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Analyzing user behavior in online spaces is an important task. This paper is dedicated to analyzing the online community in terms of topics. We present a user–topic model based on the latent Dirichlet allocation (LDA), as an application of topic modeling in a domain other than textual data. This model substitutes the concept of word occurrence in the original LDA method with user participation. The proposed method deals with many problems regarding topic modeling and user analysis, which include: inclusion of dynamic topics, visualization of user interaction networks, and event detection. We collected datasets from four online communities with different characteristics, and conducted experiments to demonstrate the effectiveness of our method by revealing interesting findings covering numerous aspects.
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Aakash, Aakash, i Ajay Jaiswal. "Segmentation and Ranking of Online Reviewer Community". International Journal of E-Adoption 12, nr 1 (styczeń 2020): 63–83. http://dx.doi.org/10.4018/ijea.2020010106.
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Online reviewer societies flourish on contributions from different reviewers, who display a wavering engagement behavior. Effort has been made in the e-marketing literature for segmenting individuals with the help of their engagement behavior. In this study, the authors segment the reviewers of a popular travel website (TripAdvisor) through k-means clustering based on three dimensions (F-frequency, H-helpfulness, R-recency), resulting in four different reviewer segments-valuable, trustworthy, new and valueless. The authors calculate the reviewer value using fuzzy AHP and then rank the reviewer segment accordingly. The authors find that the valuable reviewers, who post eWOM regularly and get greater helpful votes by eWOM readers, are the most important. Surprisingly, the trustworthy, who also get more helpful votes with higher eWOM volume, but not posting any review recently, are the second most important. This research is a novel effort on reviewer segmentation and gives valuable insights to e-marketers.