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1
Hu, Renjie, and Guangyu Zhang. "Structural Holes in Directed Fuzzy Social Networks." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/452063.
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The structural holes have been a key issue in fuzzy social network analysis. For undirected fuzzy social networks where edges are just present or absent undirected fuzzy relation and have no more information attached, many structural holes measures have been presented, such as key fuzzy structural holes, general fuzzy structural holes, strong fuzzy structural holes, and weak fuzzy structural holes. There has been a growing need to design structural holes measures for directed fuzzy social networks, because directed fuzzy social networks where edges are attached by directed fuzzy relation would contain rich information. In this paper, we extend structural holes theory to directed fuzzy social network and propose the algorithm of unidirectional fuzzy structural holes and bidirectional fuzzy structural holes, which unveil more structural information of directed fuzzy social networks. Furthermore, we investigate the validness of the algorithm by illustrating this method to a case calledG-Y Research Teamand obtain reliable results, which provide strong evidences of the new measure’s utility.
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Cheng, Yong. "Crowd-Sourcing Information Dissemination Based on Spatial Behavior and Social Networks." Mobile Information Systems 2021 (March 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/6652740.
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In the context of today’s network era, rich social networks and convenient network communication make different individuals and groups interact and transmit information in more diversified ways, which also bring new dissemination in information of crowd-sourcing tasks. The paper analyzes mobile behavior characteristics of users from different perspectives, such as spatial activity behavior and location type preference, and constructs a user space mobile behavior model based on the physical world. At the same time, it analyzes the social influence of users in social networks and mode of information transmission. In the paper, real data sets are adopted, mathematical modeling and computer simulation are combined to build an information communication model around the social influence of users in social networks, and the rules of information communication in new environment of social networks are depicted in combination with users’ spatial movement.
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Zenkovich, K., T. Zhylkybayev, S. Kaysanov, and T. Ustinova. "APPLYING SOCIAL MINING RESULTS FROM OPEN SOCIAL NETWORKS." Bulletin of Shakarim University. Technical Sciences 1, no. 2(14) (2024): 5–10. http://dx.doi.org/10.53360/2788-7995-2024-2(14)-1.
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TThe advent of web-based communities and social networking sites has resulted in a massive amount of social networking data that is embedded with rich sets of meaningful social media knowledge. Social network analysis and the study of social structures using networks and graph theory help to find a systematic method or process for studying social networks. The article reveals the concept of intellectual analysis of social networks. The key aspect of the article is the application of the results of social network analysis to various branches of human activity.Describes the benefits of using Social Mining to identify patterns in big data. Using Social Mining mechanisms, you can find non-trivial and, at first glance, non-obvious patterns in large volumes of information. The article provides examples of software that can be used to quickly collect and analyze data from social networks. Analytics services simplify work and increase opportunities on social networks. Social network analysis provides an effective system for discovering and interpreting online social connections.Social network analytics goes beyond counting likes, reposts and links. This is a comprehensive indepth data analysis that helps to understand what attracts more attention or guide users when accessing the brand through social networks.
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Lerman, Kristina, and Rumi Ghosh. "Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 90–97. http://dx.doi.org/10.1609/icwsm.v4i1.14021.
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Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.
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Kataev, M. Yu, and V. V. Orlova. "Social media event data analysis." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 4 (2020): 71–77. http://dx.doi.org/10.21293/1818-0442-2020-23-4-71-77.
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Social media analysis has become ubiquitous at a quantitative and qualitative level due to the ability to study content from open social networks. This content is a rich source of data for the construction and analysis of the interaction of social network users when forming various groups, used not only for statistical calculations, social areas of analysis, but also in trade or for the development of recommendation systems. The large number of social media users results in a huge amount of unstructured data (by time, type of communication, type of message and geographic location). This article aims to discuss the problem of analyzing social networks and obtaining information from unstructured data. The article discusses information extraction methods, well-known software products and datasets.
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Qu, Zheng, Qingyao Jia, Chen Lyu, Jia Liu, Xiaoying Liu, and Kechen Zheng. "Detecting Fake Reviews with Generative Adversarial Networks for Mobile Social Networks." Security and Communication Networks 2022 (November 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/1164125.
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With the growth of mobile social networks (MSNs), crowdsourced information could be used for recommendation to mobile users. However, it is quite vulnerable to Sybil attacks, where attackers post fake information or reviews to mislead users for business benefits. To address this problem, existing detection models mainly use graph-based techniques or extract features of users. However, these approaches either rely on strong assumptions or lack generalization. Therefore, we propose a novel Sybil detection model based on generative adversarial networks (GANs), which contains a feature extractor, a domain classifier, and a Sybil detector. First, the feature extractor is proposed to identify the rich information in the review text with the neural network model of TextCNN. Second, the domain classifier is implemented by a neural network discriminator and is able to extract common features. Third, the Sybil detector is utilized to discriminate the fake review. Finally, the minimax game between the domain classifier and Sybil detector forms a GAN and enhances the overall generalization ability of the model. Extensive experiments show that our model has a high detection accuracy against Sybil attacks.
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Lai, Yi-Yu, Jennifer Neville, and Dan Goldwasser. "TransConv: Relationship Embedding in Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4130–38. http://dx.doi.org/10.1609/aaai.v33i01.33014130.
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Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Traditional knowledge graph embedding models learn continuous low-dimensional embedding of entities and relations. However, when applied to social networks, existing approaches do not consider the rich textual communications between users, which contains valuable information to describe social relationships. In this paper, we propose TransConv, a novel approach that incorporates textual interactions between pair of users to improve representation learning of both users and relationships. Our experiments on real social network data show TransConv learns better user and relationship embeddings compared to other state-of-theart knowledge graph embedding models. Moreover, the results illustrate that our model is more robust for sparse relationships where there are fewer examples.
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Forghani, M., and F. Karimipour. "EXTRACTING HUMAN BEHAVIORAL PATTERNS BY MINING GEO-SOCIAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 115–20. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-115-2014.
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Accessibility of positioning technologies such as GPS offer the opportunity to store one’s travel experience and publish it on the web. Using this feature in web-based social networks and considering location information shared by users as a bridge connecting the users’ network to location information layer leads to the formation of Geo-Social Networks. The availability of large amounts of geographical and social data on these networks provides rich sources of information that can be utilized for studying human behavior through data analysis in a spatial-temporal-social context. This paper attempts to investigate the behavior of around 1150 users of Foursquare network by making use of their check-ins. The authors analyzed the metadata associated with the whereabouts of the users, with an emphasis on the type of places, to uncover patterns across different temporal and geographical scales for venue category usage. The authors found five groups of meaningful patterns that can explore region characteristics and recognize a number of major crowd behaviors that recur over time and space.
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Bai, Zhongbo, and Xiaomei Bai. "Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks." Complexity 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/5743825.
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With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.
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Bai, Zhongbo, and Xiaomei Bai. "Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks." Complexity 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/5743825.
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With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.
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Zhao, Yan, Weifeng Rao, Zihui Hu, and Qi Zheng. "Research on Heterogeneous Information Network Link Prediction Based on Representation Learning." Journal of Electronic Research and Application 8, no. 5 (2024): 32–37. http://dx.doi.org/10.26689/jera.v8i5.8486.
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A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and the theoretical basis of representation learning, and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail. The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.
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Xie, Hui, Guangjian Li, Yongjie Yan, and Sihui Shu. "Evolution of Bounded Confidence Opinion in Social Networks." Discrete Dynamics in Nature and Society 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/3173016.
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We investigate opinion dynamics as a stochastic process in social networks. We introduce the stubborn agent in order to determine the impact of network structure on the emergence of consensus. Depending on the fraction of undirected long-range connections, we observe fascinatingly rich dynamical behavior and transitions from disordered to ordered states. In general, we find that the stubborn agent promotes the emergence of consensus due to the so-called “group effect” that facilitates coalescence between separated network components. Agents are also behaviorally constrained Shannon information entropy in networks. However, since agents want to evolve their opinion with Brownian motion, which may in turn impede full consensus, sufficiently frequent long-range links are in such situations crucial for the network to converge into an absorbing phase. Our experimental findings indicate that, for a large range of control parameters, our model yields stable and fluctuating polarized states.
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Lahiri, Mayank, and Manuel Cebrian. "The Genetic Algorithm as a General Diffusion Model for Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 494–99. http://dx.doi.org/10.1609/aaai.v24i1.7677.
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Diffusion processes taking place in social networks are used to model a number of phenomena, such as the spread of human or computer viruses, and the adoption of products in viral marketing campaigns. It is generally difficult to obtain accurate information about how such spreads actually occur, so a variety of stochastic diffusion models are used to simulate spreading processes in networks instead. We show that a canonical genetic algorithm with a spatially distributed population, when paired with specific forms of Holland's synthetic hyperplane-defined objective functions, can simulate a large and rich class of diffusion models for social networks. These include standard diffusion models, such as the Independent Cascade and Competing Processes models. In addition, our Genetic Algorithm Diffusion Model (GADM) can also model complex phenomena such as information diffusion. We demonstrate an application of the GADM to modeling information flow in a large, dynamic social network derived from e-mail headers.
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Tan, Shulong, Jiajun Bu, Chun Chen, Bin Xu, Can Wang, and Xiaofei He. "Using rich social media information for music recommendation via hypergraph model." ACM Transactions on Multimedia Computing, Communications, and Applications 7S, no. 1 (2011): 1–22. http://dx.doi.org/10.1145/2037676.2037679.
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Azzaz, Amina, Mimoun Malki, Zohra Slama, and Nassim Dennouni. "Social Information Retrieval using Linked Data and Deep Learning." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23360–66. https://doi.org/10.48084/etasr.10551.
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Online Social Networks (OSNs) are becoming increasingly important in business, government, and all areas of life. For-profit companies use them as rich sources of information and dynamic platforms to drive strategies in product design, innovation, relationship management, and marketing. However, analyzing and retrieving information from these platforms presents distinct challenges due to their inherent characteristics and dynamic nature. To address this, researchers have proposed various approaches for social information retrieval, ranging from term-based analysis to semantic-based methods. To overcome the limitations of existing techniques, the present study proposes a multilayer model that integrates graph analysis, semantic content, and deep learning. The general proposed approach is also presented. By combining learning-to-rank techniques with linked data, a robust framework for social information retrieval is constructed. This method enables a more nuanced understanding by leveraging both the rich contextual information provided by linked data and the structural characteristics of social networks. The proposed model is a flexible framework that can be easily extended to add or remove features and can be applied to various tasks. The experimental results confirm the effectiveness and efficiency of the proposed approach.
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Jain, Ramesh. "EventWeb: towards social life networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1987 (2013): 20120384. http://dx.doi.org/10.1098/rsta.2012.0384.
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The Web has changed the way we live, work and socialize. The nodes in the current Web are documents and hence the current World Wide Web is a Document Web. Advances in technology and requirements of emerging applications require formation of a parallel and closely connected Web of events, the EventWeb, in which each node is an event. In this paper, we explore growth of EventWeb as a natural next step in the evolution of the Web with rich multimodal sensory information. Social networks use events extensively and have revolutionized communication among people. Mobile phones, equipped with myriads of sensors and being used by more than 75% of living humans, are bringing the next generation of social networks, not only to connect people with other people, but also to connect people with other people and essential life resources. We call these networks social life networks, and believe that this is the right time to focus efforts to discover and develop technology and infrastructure to design and build these networks and to apply them for solving some essential human problems.
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Wu, Jibing, Zhifei Wang, Yahui Wu, Lihua Liu, Su Deng, and Hongbin Huang. "A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms." Scientific Programming 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2803091.
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Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.
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Xing, Deng, Wu, Xie, and Gao. "Behavioral Habits-Based User Identification Across Social Networks." Symmetry 11, no. 9 (2019): 1134. http://dx.doi.org/10.3390/sym11091134.
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Social networking is an interactive Internet of Things. The symmetry of the network can reflect the similar friendships of users on different social networks. A user’s behavior habits are not easy to change, and users usually have the same or similar display names and published contents among multiple social networks. Therefore, the symmetry concept can be used to analyze the information generated by the user for user identification. User identification plays a key role in building better information about social network user profiles. As a consequence, it has very important practical significance in many network applications and has attracted a great deal of attention from researchers. However, existing works are primarily focused on rich network data and ignore the difficulty involved in data acquisition. Display names and user-published content are very easy to obtain compared to other types of user data across different social networks. Therefore, this paper proposes an across social networks user identification method based on user behavior habits (ANIUBH). We analyzed the user’s personalized naming habits in terms of display names, then utilized different similarity calculation methods to measure the similarity of the features contained in the display names. The variant entropy value was adopted to assign weights to the features mentioned above. In addition, we also measured and analyzed the user’s interest graph to further improve user identification performance. Finally, we combined one-to-one constraint with the Gale–Shapley algorithm to eliminate the one-to-many and many-to-many account-matching problems that often occur during the results-matching process. Experimental results demonstrated that our proposed method enables the possibility of user identification using only a small amount of online data.
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Gafarov, Fail, Andrey Berdnikov, and Pavel Ustin. "Online social network user performance prediction by graph neural networks." International Journal of Advances in Intelligent Informatics 8, no. 3 (2022): 285. http://dx.doi.org/10.26555/ijain.v8i3.859.
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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use of graph convolutional neural networks with different convolution layers (GCNConv, SAGEConv, GraphConv, GATConv, TransformerConv, GINConv) for predicting the user’s professional success in VKontakte online social network, based on data obtained from his profiles. We have used various parameters obtained from users’ personal pages in VKontakte social network (the number of friends, subscribers, interesting pages, etc.) as their features for determining the professional success, as well as networks (graphs) reflecting connections between users (followers/ friends). In this work we performed graph classification by using graph convolutional neural networks (with different types of convolution layers). The best accuracy of the graph convolutional neural network (0.88) was achieved by using the graph isomorphism network (GIN) layer. The results, obtained in this work, will serve for further studies of social success, based on metrics of personal profiles of OSN users and social graphs using neural network methods.
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Gao, Huiji, Jiliang Tang, and Huan Liu. "Exploring Social-Historical Ties on Location-Based Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 114–21. http://dx.doi.org/10.1609/icwsm.v6i1.14240.
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Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.
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Walter, Nathan, Sandra J. Ball-Rokeach, Yu Xu, and Garrett M. Broad. "Communication Ecologies: Analyzing Adoption of False Beliefs in an Information-Rich Environment." Science Communication 40, no. 5 (2018): 650–68. http://dx.doi.org/10.1177/1075547018793427.
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The continued fragmentation of information and the proliferation of communication resources necessitate a shift toward perspectives that situate communication practices in a multilevel ecosystem. The current article offers a method to map and analyze communication ecologies—defined as the networks of communication connections that individuals depend on in order to construct knowledge and achieve goals—as social networks. To demonstrate the potential of communication ecologies as an analytical tool in science communication, we report on the results of a feasibility study ( N = 654) in the context of climate science and vaccine safety. The article discusses the theoretical and practical implications of the communication ecology approach.
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Dong, Jian, Bin Chen, Pengfei Zhang, et al. "Evolution Model of Spatial Interaction Network in Online Social Networking Services." Entropy 21, no. 4 (2019): 434. http://dx.doi.org/10.3390/e21040434.
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The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities.
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Liu, Xiaozhong, Tian Xia, Yingying Yu, Chun Guo, and Yizhou Sun. "Cross Social Media Recommendation." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (2021): 221–30. http://dx.doi.org/10.1609/icwsm.v10i1.14714.
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The proliferation of rich social media data revolutionizes the way people perceive and understand the world. Unfortunately, so far, there does not exist a single social media system that efficiently globalizes users around the world. Two well-known social media systems, Twitter and Facebook, are strictly blocked in mainland China for political reasons, which means 21.97% of Internet users are excluded from these systems. Similarly, the second-largest microblogging system in the world, Sina Weibo, features a default system language of Chinese, which rules out many users from other countries. This creates what we call language, network, and culture bubbles. As a result, if we are interested in modeling the knowledge of the world, all research findings based on a single social media system (within a bubble) can be biased, and the social networks or knowledge networks generated from a single system or social community cannot fully represent people from around the world. In this study, we generate a pseudo-social heterogeneous network - Pseudo Global Social Media Network (PGSMN), which bridges the topics of Twitter and Weibo. On this network, all Weibo and Twitter nodes are interconnected via an interim knowledge layer, and user or topic nodes from Twitter can randomly walk to the nodes on Weibo (via different kinds of paths), and vice versa, which enables cross-network information recommendation and knowledge globalization.
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Aljably, Randa, Yuan Tian, and Mznah Al-Rodhaan. "Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection." Security and Communication Networks 2020 (July 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/5874935.
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Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.
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Xue, Di, Li-Fa Wu, Hua-Bo Li, Zheng Hong, and Zhen-Ji Zhou. "A novel destination prediction attack and corresponding location privacy protection method in geo-social networks." International Journal of Distributed Sensor Networks 13, no. 1 (2017): 155014771668542. http://dx.doi.org/10.1177/1550147716685421.
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Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework.
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Gernat, Tim, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld, and Gene E. Robinson. "Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks." Proceedings of the National Academy of Sciences 115, no. 7 (2018): 1433–38. http://dx.doi.org/10.1073/pnas.1713568115.
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Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.
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Tosun, Nilgun, and Aynur Gecer. "A Development, Validity and Reliability of Safe Social Networking Scale." ATHENS JOURNAL OF MASS MEDIA AND COMMUNICATIONS 8, no. 3 (2022): 179–200. http://dx.doi.org/10.30958/ajmmc.8-3-3.
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Social networks has become in the spotlight for millions of people since they create a dynamic and rich interaction environment between users, and allow free access and usage of fake identity information. Distance education and working from home around the world due to COVID-19 pandemic has affected the increase in the rate and duration of social media usage in the last year. Unfortunately, this increase has offered criminals, who have the potential to pose risks and dangers on social media, more opportunities. Therefore, it has become even more important to use social media as a safe environment. The objective of this study was to develop a Safe Social Networking Scale for determining the security levels in social network usage. The validity and reliability studies of the scale were conducted with 585 social media users. As a result of the validity study, 28 items under five factors were obtained. These factors were being “Time Tunnel/Wall Sharing”, “Safety of Social Network Profile Information and Sharing”, “Social Network Friends List and its Safety”, “Safety of Social Network Information Input”, “Safe Login to Social Network Account”. The items obtained were capable of discriminating the individuals in terms of the features to be measured by the scale. There has not been any scale that directly determines the level of safe use of social media in the literature. The developed scale is expected to fill this scale gap in the literature. Keywords: safety, social networks, scale development, reliability, validity
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Vadisala, Jyothi, and Valli Kumari Vatsavayi. "Privacy Preservation in Sequential Published Social Networks Against Mutual Friend Attack." International Journal of Engineering & Technology 7, no. 4 (2018): 3731. http://dx.doi.org/10.14419/ijet.v7i4.17424.
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In recent years the social networks are widely used the way of connecting people, interact with each other and share the information. The social network data is rich in content and the data are published for third party users such as researchers. The social interaction between individual’s changes rapidly as time changes so there is a need of privacy preserving in dynamic networks. An adversary can acquire some local knowledge about individuals in the network and can easily breach the privacy of a few victims. This paper mainly focuses on preserving privacy in sequential published network data where the adversary has some knowledge about the number of mutual friends of the target victims over a time period. The kw-Number of Mutual Friend Anonymization model is proposed to anonymize each sequential published network. In this privacy model, k indicates the privacy level and w is the time interval taken by the adversary to acquire the knowledge of the victim. By this approach the adversary cannot identify the victim by acquiring the knowledge of each sequential published data. The performance evaluation shows that the proposed approach can preserve many characteristics of the dynamic social networks.
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Cai, Xiaokang, Ruoyuan Gong, and Hao Jiang. "Multilevel Context Learning with Large Language Models for Text-Attributed Graphs on Social Networks." Entropy 27, no. 3 (2025): 286. https://doi.org/10.3390/e27030286.
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There are complex graph structures and rich textual information on social networks. Text provides important information for various tasks, while graph structures offer multilevel context for the semantics of the text. Contemporary researchers tend to represent these kinds of data by text-attributed graphs (TAGs). Most TAG-based representation learning methods focus on designing frameworks that convey graph structures to large language models (LLMs) to generate semantic embeddings for downstream graph neural networks (GNNs). However, these methods only provide text attributes for nodes, which fails to capture the multilevel context and leads to the loss of valuable information. To tackle this issue, we introduce the Multilevel Context Learner (MCL) model, which leverages multilevel context on social networks to enhance LLMs’ semantic embedding capabilities. We model the social network as a multilevel context textual-edge graph (MC-TEG), effectively capturing both graph structure and semantic relationships. Our MCL model leverages the reasoning capabilities of LLMs to generate semantic embeddings by integrating these multilevel contexts. The tailored bidirectional dynamic graph attention layers are introduced to further distinguish the weight information. Experimental evaluations on six real social network datasets show that the MCL model consistently outperforms all baseline models. Specifically, the MCL model achieves prediction accuracies of 77.98%, 77.63%, 74.61%, 76.40%, 72.89%, and 73.40%, with absolute improvements of 9.04%, 9.19%, 11.05%, 7.24%, 6.11%, and 9.87% over the next best models. These results demonstrate the effectiveness of the proposed MCL model.
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Golędzinowski, Wojciech, and Władysław Błocki. "SOCIAL NETWORK ANALYSIS: FROM GRAPH THEORY TO APPLICATIONS." Social Communication 24, no. 1 (2024): 151–64. https://doi.org/10.57656/sc-2023-0012.
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Social Network Analysis (SNA) is a powerful interdisciplinary field that explores the patterns and dynamics of relationships between individuals, groups, organizations, and even entire societies. This article provides an overview of SNA, tracing its roots in graph theory and highlighting its various applications in fields such as sociology, computer science, business, and epidemiology. By examining the theoretical foundations of SNA and its practical implementations, this article aims to demonstrate the importance of SNA in understanding social structures, information diffusion, impact dynamics, and collective behavior. In addition, the article discusses the methodologies and tools used in SNA research, including data collection, network visualization and network metrics. Through a comprehensive analysis of SNA techniques and their applications, this article contributes to the growing knowledge in the analysis of social networks and encourages further exploration of this rich field.
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Liu, Yanni, Dongsheng Liu, and Yuwei Chen. "Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City." Complexity 2020 (June 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/9789431.
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With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.
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Shields, Thomas, Hannah Li, Peter Lebedev, and Josiah Dykstra. "Cyber Buzz: Examining Virality Characteristics of Cybersecurity Content In Social Networks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 441–45. http://dx.doi.org/10.1177/1071181320641099.
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The Internet is a rich environment for information to spread rapidly and widely. The ability of cybersecurity content to achieve virality in social networks can be useful for measuring security awareness, policy adoption, or cybersecurity literacy. It may also reveal new and emerging cybersecurity events. Virality in online social networks can be characterized and measured many ways and have different causes. Leveraging existing research in social network virality measurements, we calculate and analyze virality measurements and correlations on an anonymized Reddit dataset, examining overall trends and characteristics of individual cybersecurity forums (subreddits). We reproduce content-based virality prediction algorithms and assess their performance, then introduce additional features beyond post title, including time of day, to improve prediction accuracy to ~71% for each of the virality scores. We examine the intersection of the virality facets to reveal correlations about the content and times when cybersecurity content is most viral.
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Su, Xiaoping, Yinghua Zha, and Fuquan Zhang. "SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship." Applied Sciences 15, no. 8 (2025): 4520. https://doi.org/10.3390/app15084520.
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Many complex social systems can be modeled as directed signed networks whose edges are marked with a positive/negative sign or direction. Network embedding representation is aimed at mapping rich structural and semantic information into low-dimensional vectors, and extensive research has demonstrated that Graph Neural Networks (GNNs) are an effective way. However, existing GNNs are primarily designed for undirected signed networks and usually used to capture the semantics of the complex structure by social structural balance theory, thus omitting the directional information of the links. In this research, we introduce a reciprocity relationship and status theory to enhance the modeling of the directed positive/negative relationship between two nodes, which has been widely applied in complex network research, and design SR-GNN, a GNN model for signed directed networks, to enable a more accurate vector representation of the nodes and convolution operations on edges with different directions and signs. Experiments demonstrate a reciprocity relationship, and status theory can allow the model to extract the most essential comprehensive information in signed directed graphs. Furthermore, SR-GNN can obtain effective status scores of nodes for link sign predictions and node ranking tasks, both of which yield state-of-the-art performance in most cases.
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Chang, Wanjun, and Shaohui Ma. "A Sentiment Analysis Model Based on Attention Map Convolutional Network." International Journal of Information Technologies and Systems Approach 17, no. 1 (2024): 1–14. http://dx.doi.org/10.4018/ijitsa.348658.
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Sina Weibo has evolved into a daily social tool for people, yet effectively leveraging its data for sentiment analysis remains a challenging task due to the presence of information beyond text, such as emojis or images. In this paper, we propose an attention graph convolutional network (AGCN) for fine-grained sentiment classification of Weibo posts. Utilizing an attention network based on cosine similarity, the rich emotional information embedded in emoji features interacts with the textual content, effectively enhancing the capability to represent emotions in the text. Leveraging the characteristics of attention networks to construct a graph structure effectively enables graph convolutional networks to capture higher-order relationships between words in textual features. This approach addresses the challenge of extracting sentiment tendencies from Weibo comments. Experimental results on public data sets demonstrate the effectiveness of AGCNs.
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Ivanova, Aneliya, and Elitsa Ibryamova. "The Role of Social Networks and Micro-Learning in the Digitalization of Education." Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika 31, no. 4s (2023): 120–34. http://dx.doi.org/10.53656/str2023-4s-10-the.
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As social networks have evolved beyond their original purpose, and have proven to be a successful platform even for business and politics, it was only natural for the educational process to find its place in this virtual realm. As early as 2019, Facebook introduced the Social Learning mode, which provided excellent opportunities for publishing and structuring educational content within closed groups, taking the utilization of this social network to a new level in the learning process. It can hardly be disputed that social networks have the potential to offer rich access to a variety of educational resources, but recently, there has been a persistent migration of students from the digital generation to platforms such as Instagram, TikTok, Pinterest, where digital content in the form of small portions of visual and video information circulates. This trend leads to the need for a fresh interpretation of the concept of micro-learning and micro-content in the context of social networks, as well as the need to explore learners' attitudes towards such type of learning.
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Madsen, Dorte. "Shall We Dance?" Journal of Information Architecture 1, no. 1 (2009): 1–6. http://dx.doi.org/10.55135/1015060901/091.001/1.001.
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It is with great pride that I welcome you to the inaugural issue of the Journal of Information Architecture. The Journal of Information Architecture is a peer-reviewed scholarly journal, and its aim is to facilitate the systematic development of the scientific body of knowledge in the field of information architecture. The journal will focus on information architecture research and development in all types of shared information environments, such as for example social networks, web sites, intranets, mobile and Rich Internet Applications, from various perspectives such as technical, cultural, social, and communicational
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Jabbar, Huriya, Rachel Boggs, and Joshua Childs. "Race, Gender, and Networks: How Teachers’ Social Connections Structure Access to Job Opportunities in Districts With School Choice." AERA Open 8 (January 2022): 233285842210847. http://dx.doi.org/10.1177/23328584221084719.
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Research in sociology demonstrates the way social connections shape access to information about job opportunities. In education, we understand less about how social networks impact the job process for marginalized teachers and teachers in nontraditional labor markets. This study examines how teachers in New Orleans and Detroit, cities with high concentrations of charter schools, use their networks to search for jobs, and how their experiences vary by race and gender. We find that in choice-rich environments, there was an extensive reliance on social networks in the hiring process, and teachers had different access to key social networks that can help to land jobs. Hiring decisions and unequal access to job opportunities among teacher candidates, in part due to the reliance on networks, created conditions where teachers who cultivated stronger networks, or with access to the “right” networks, had greater opportunity, with implications for racial and gender equity and diversity.
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Hasin, Hajar Ali, and Diman Hassan. "Link Prediction in Co-authorship Networks." Science Journal of University of Zakho 10, no. 4 (2022): 235–57. http://dx.doi.org/10.25271/sjuoz.2022.10.4.1040.
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Besides social network analysis, the Link-Prediction (LP) problem has useful applications in information retrieval, bioinformatics, telecommunications, microbiology, and e-commerce as a forecast of future links in a given context to find what possible connections are based on a local and global statistical analysis of the given graph data. However, in Academic Social Networks (ASNs), the LP issue has recently attracted a lot of attention in academia and called for a variety of link prediction techniques to predict co-authorship among researchers and to examine the rich structural and associated data. As a result, this study investigates the problem of LP in ASNs to forecast the upcoming co-authorships among researchers. In a systematic approach, this review presents, analyses, and compares the primary taxonomies of topological-based, content-based, and hybrid-based approaches, which are used for computing similar scores for each pair of unconnected nodes. Then, this study ends with findings on challenges and open problems for the community to work on for further development of the LP problem of scholarly social networks.
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Wu, Qingqing, Xianguan Zhao, Lihua Zhou, Yao Wang, and Yudi Yang. "Minimizing the influence of dynamic rumors based on community structure." International Journal of Crowd Science 3, no. 3 (2019): 303–14. http://dx.doi.org/10.1108/ijcs-09-2019-0025.
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Purpose With the rapid development of internet technology, open online social networks provide a broader platform for information spreading. While dissemination of information provides convenience for life, it also brings many problems such as security risks and public opinion orientation. Various negative, malicious and false information spread across regions, which seriously affect social harmony and national security. Therefore, this paper aims to minimize negative information such as online rumors that has attracted extensive attention. The most existing algorithms for blocking rumors have prevented the spread of rumors to some extent, but these algorithms are designed based on entire social networks, mainly focusing on the microstructure of the network, i.e. the pairwise relationship or similarity between nodes. The blocking effect of these algorithms may be unsatisfactory in some networks because of the sparse data in the microstructure. Design/methodology/approach An algorithm for minimizing the influence of dynamic rumor based on community structure is proposed in this paper. The algorithm first divides the network into communities, and integrates the influence of each node within communities and rumor influence probability to measure the influence of each node in the entire network, and then selects key nodes and bridge nodes in communities as blocked nodes. After that, a dynamic blocking strategy is adopted to improve the blocking effect of rumors. Findings Community structure is one of the most prominent features of networks. It reveals the organizational structure and functional components of a network from a mesoscopic level. The utilization of community structure can provide effective and rich information to solve the problem of data sparsity in the microstructure, thus effectively improve the blocking effect. Extensive experiments on two real-world data sets have validated that the proposed algorithm has superior performance than the baseline algorithms. Originality/value As an important research direction of social network analysis, rumor minimization has a profound effect on the harmony and stability of society and the development of social media. However, because the rumor spread has the characteristics of multiple propagation paths, fast propagation speed, wide propagation area and time-varying, it is a huge challenge to improve the effectiveness of the rumor blocking algorithm.
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West, Robert, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. "Exploiting Social Network Structure for Person-to-Person Sentiment Analysis." Transactions of the Association for Computational Linguistics 2 (December 2014): 297–310. http://dx.doi.org/10.1162/tacl_a_00184.
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Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.
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Paul, Sharoda, Lichan Hong, and Ed Chi. "Is Twitter a Good Place for Asking Questions? A Characterization Study." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 578–81. http://dx.doi.org/10.1609/icwsm.v5i1.14165.
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People often turn to their social networks to fulfill their information needs. We conducted a study of question asking and answering (Q&A) behavior on Twitter. We found that the most popular question types were rhetorical and factual. Surprisingly, along with entertainment and technology questions, people asked personal and health-related questions. The majority of questions received no response, while a handful of questions received a high number of responses. The larger the askers’ network, the more responses she received; however, posting more tweets or posting more frequently did not increase chances of receiving a response. Most often the ‘follow’ relationship between asker and answerer was one-way. We provide a rich characterization of Q&A in social information streams and discuss implications for design.
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Ghavasieh, A., and M. De Domenico. "Statistical physics of network structure and information dynamics." Journal of Physics: Complexity 3, no. 1 (2022): 011001. http://dx.doi.org/10.1088/2632-072x/ac457a.
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Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and computer science. On the other hand, studies at the edge of network science and quantum physics are gaining increasing attention, e.g., because of the potential applications to quantum networks for communications, such as the quantum Internet. In this work, we briefly review a novel framework grounded on statistical physics and techniques inspired by quantum statistical mechanics which have been successfully used for the analysis of a variety of complex systems. The advantage of this framework is that it allows one to define a set of information-theoretic tools which find widely used counterparts in machine learning and quantum information science, while providing a grounded physical interpretation in terms of a statistical field theory of information dynamics. We discuss the most salient theoretical features of this framework and selected applications to protein–protein interaction networks, neuronal systems, social and transportation networks, as well as potential novel applications for quantum network science and machine learning.
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BHAVANI, MRS B. DURGA, NANDIGAMA NIHARIKA, LOYAPALLY PRANITHA, and KONDU ARCHITHA. "ROBUST DETECTION OF LINK COMMUNITIES WITH SUMMARY DESCRIPTION IN SOCIAL NETWORKS." Journal of Engineering Sciences 15, no. 10 (2024): 71–81. http://dx.doi.org/10.36893/jes.2024.v15i10.010.
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Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.
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Pu, Jiansu, Jingwen Zhang, Hui Shao, Tingting Zhang, and Yunbo Rao. "egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network." Sensors 20, no. 20 (2020): 5895. http://dx.doi.org/10.3390/s20205895.
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The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.
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Li, Jingwei, Xinyi Bai, and Zhaoming Han. "DGFN Multimodal Emotion Analysis Model Based on Dynamic Graph Fusion Network." International Journal of Decision Support System Technology 16, no. 1 (2024): 1–18. http://dx.doi.org/10.4018/ijdsst.352417.
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In recent years, integrating text and image data for sentiment analysis in social networks has become a key approach. However, techniques for capturing complex cross-modal information and effectively fusing multimodal features still have shortcomings. We design a multimodal sentiment analysis model called the Dynamic Graph-Text Fusion Network (DGFN) to address these challenges. Text features are captured by leveraging the neighborhood information aggregation properties of Graph Convolutional Networks, treating words as nodes and integrating their features through their adjacency relationships. Additionally, the multi-head attention mechanism is utilized to extract rich semantic information from different subspaces simultaneously. For image feature extraction, a convolutional attention module is employed. Subsequently, an attention-based fusion module integrates the text and image features. Experimental results on the two datasets show significant improvements in sentiment classification accuracy and F1 scores, validating the effectiveness of the proposed DGFN model.
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Diaz, Juglar, Felipe Bravo-Marquez, and Barbara Poblete. "Language Modeling on Location-Based Social Networks." ISPRS International Journal of Geo-Information 11, no. 2 (2022): 147. http://dx.doi.org/10.3390/ijgi11020147.
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The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places.
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Hallac, Ibrahim Riza, Betul Ay, and Galip Aydin. "User Representation Learning for Social Networks: An Empirical Study." Applied Sciences 11, no. 12 (2021): 5489. http://dx.doi.org/10.3390/app11125489.
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Gathering useful insights from social media data has gained great interest over the recent years. User representation can be a key task in mining publicly available user-generated rich content offered by the social media platforms. The way to automatically create meaningful observations about users of a social network is to obtain real-valued vectors for the users with user embedding representation learning models. In this study, we presented one of the most comprehensive studies in the literature in terms of learning high-quality social media user representations by leveraging state-of-the-art text representation approaches. We proposed a novel doc2vec-based representation method, which can encode both textual and non-textual information of a social media user into a low dimensional vector. In addition, various experiments were performed for investigating the performance of text representation techniques and concepts including word2vec, doc2vec, Glove, NumberBatch, FastText, BERT, ELMO, and TF-IDF. We also shared a new social media dataset comprising data from 500 manually selected Twitter users of five predefined groups. The dataset contains different activity data such as comment, retweet, like, location, as well as the actual tweets composed by the users.
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Dentoni, Domenico, and Thomas Reardon. "Small farms building global brands through social networks." Journal on Chain and Network Science 10, no. 3 (2010): 159–71. http://dx.doi.org/10.3920/jcns2010.x183.
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Small farms have the option of competing in the global market by pursuing a niche brand differentiation strategy. However, they usually face tight financial constraints when attempting to build a food brand that meets both the desires of a small segment of distant final consumers and the requirements of its international buyers. In this study, we explore how small farms can use social networks to start transacting with international buyers and to build global niche brands. Following a 'grounded theory' approach, we analyzed the evidence collected from 34 cases of small farms producing single-estate extra-virgin olive oil and other specialty food products in Italy. The analysis led to the following conclusions. First, small olive oil farmers can build brand associations and perceived brand quality, and ultimately brand equity, by developing social ties with third-party endorsers that are outside the product supply chain but have high status in the market. Second, to intentionally develop these social ties, small olive oil farmers need to obtain information both on (a) international consumer preferences for olive oil attributes and (b) which actors have the high status to endorse and promote the individual brands. Third, use of social ties with high-status endorsers for brand development is more effective when international consumers' familiarity with the product is lower and their preference for credence attributes stronger. While concerning a developed country that moreover enjoys a strong reputation in relation to the product, we posit that this study is rich with lessons for small producers of specialty food in both developed and developing regions whose reputations associated with the specific products are high. From a policy perspective, this study suggests that public market development programs can play a key facilitation role for the development of social networks linking small companies and international buyers by providing relevant market information on third-party endorsers as well as final consumers and buyers.
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Noorizadeh Ghasri, Atefeh, Seyyed Aliakbar Famil Rouhany, and Nasrolah Erfani. "Subjective Commentary of Health Literacy through Social Networks for Civil Servants’ Pension Fund Beneficiaries: A Qualitative Study." Depiction of Health 12, no. 2 (2021): 178–86. http://dx.doi.org/10.34172/doh.2021.18.
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Background and Objectives: Elderly people need to pay more attention to promoting health promotion and improving quality of life in comparison with other people. On the other hand, the interest and desire to work with the Internet and social networks of internet plays an indelible role in improving the health literacy of the community. This study was carried out with the aim of evaluation the subjective explanation of health literacy through social networks for retired of fund beneficiaries. Material and Methods: The present study is a phenomenological study with emphasis on Van Mennen's perspective to discover the experiences of retirees from the phenomenon of health literacy through social networks. The data were collected through a deep interview. Semi-structured interviews were conducted with 15 retirees of the State Pension Fund in Tehran in 2020 using purposive sampling. All interviews were recorded and implemented and the theme analysis method was used to analyze the interviews. Results: Data analysis resulted in the extraction of 71 primary codes and 33 sub-themes, which was classified in four main themes of experience in the field of "access to health information", experience in the field of "understanding health information" "Experience in the field of" health information evaluation ", experience in the field of" application of health information ". Conclusion: Retirees, who make a large part of the country's elderly population, are embedded in social networks, and all organizations that play a role in the health and education of retirees can create rich educational content and using Simple, understandable multimedia content by social networks as well as the introduction of networks with reliable information for retirees, to improve the level of health literacy and increase useful health information for them, which is an effective factor in maintaining health and increasing the quality of life.
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Schreurs, Bieke, Antoine Van den Beemt, Nienke Moolenaar, and Maarten De Laat. "Networked individualism and learning in organizations." Journal of Workplace Learning 31, no. 2 (2019): 95–115. http://dx.doi.org/10.1108/jwl-05-2018-0070.
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Purpose This paper aims to investigate the extent professionals from the vocational sector are networked individuals. The authors explore how professionals use their personal networks to engage in a wide variety of learning activities and examine what social mechanisms influence professionals’ agency to form personal informal learning networks. Design/methodology/approach This study applied a mixed-method approach to data collection. Social network data were gathered among school professionals working in the vocational sector. Ego-network analysis was performed. A total of 24 in-depth, semi-structured, qualitative interviews were analyzed. Findings This study found that networked individualism is not represented to its full potential in the vocational sector. However, it is important to form informal learning ties with different stakeholders because all types of informal learning ties serve different learning purposes. The extent to which social mechanisms (i.e. proximity, trust, level of expertise and homophily) influence professionals’ agency to form informal learning ties differs depending on the stakeholder with whom the informal learning ties are formed. Research limitations/implications This study excludes the investigation of social mechanisms that shape learning through more impersonal virtual learning resources, such as social media or expert forums. Moreover, the authors only included individual- and dyadic-level social mechanisms. Practical implications By investigating the social mechanisms that shape informal learning ties, this study provides insights how professionals can be stimulated to build rich personal learning networks in the vocational sector. Originality/value The authors extend earlier research with in-depth information on the different types of learning activities professionals engage in in their personal learning networks with different stakeholders. The ego-network perspective reveals how different social mechanisms influence professionals’ agency to shape informal learning networks with different stakeholders.