Идея и реальность научной коммуникации в академических социальных сетях

The impact of the World Wide Web goes beyond communicating between computers. The web changed the way we interact with organizations, people, documents, and data. Social media is one of the new buzzwords of the web. It is a comparatively new term that covers a wide range of online applications, platforms, and media that support online interactions, collaboration, and the sharing of content. It includes all that constitutes human interaction in online platforms. Social media has had grown explosively in a short time and has a profound impact on advertising, publicity, marketing, public opinion, entertainment, software services, and decision-making. The content offered in social networks is exciting to users. However, it is challenging, as users need to spend time and effort finding content that might be of their interest. Navigating such an extensive collection of items becomes difficult if there are no tools to help the users. To overcome this problem, social media relies on data mining solutions such as recommender systems and information retrieval systems. In particular, recommender systems have found applications in many domains including movies, music, videos, news, books, and products in general. Depending on the context they produce a list of recommended items using a variety of techniques. The goal of this dissertation is to research new approaches in recommender systems that help to satisfy the needs of users in social media. Specifically, we examine the role of recommender systems for users in two types of social media, Location-Based Social Networks and academic social networks. LBSNs enable their users to share the places they go to and with whom they are. Academic social networks help modern researchers organize their scientific libraries and discover relevant papers to their research. For both types of social media most of the existing work focuses on recommendations for individual users. The proposed approaches are distinguishable from others in that we focus on providing recommendations to a group of users, rather than to individuals. Moreover, we investigate the importance of item-to-item recommendations and propose a new method for recommending on infrequent items.

Online universities utilize academic social networks to build connections among students, faculty, and alumni through affinity groups. This study explored how students interact in academic social networks, who they collaborate with, why they use academic social networks, and how this influences their educational experience. This qualitative, interpretive, phenomenological study explored the lived experiences of six online higher education students reporting active participation in an academic social network. Three core themes emerged from data analysis: (a) acceptance and belonging; (b) self-validation; and (c) drawing from multiple perspectives describing how academic social networking communities are formed, why students are using them, and what this means to online higher education. The essence of academic social networking as it relates to self-actualization is discussed, with insights for educational leaders regarding the use of academic social networking and affinity groups in online higher education.

The evolution of Social Networks during the last decades has been characterized with a plethora of research activities, that attempt to introduce pervasive features in order to enhance users' experience. However, there has been minimum effort regarding Academic Social Networks. One of the most studied features, is the recommendation of researchers for possible collaboration. In this paper, in order to enhance researchers' experience on Academic Social Networks, we introduce the use of communities. We also present the proposed systems architecture including Business Processes and Services.

Nowadays, social networks are gaining prominence in the day-to-day lives. In these networks, different relationships are established between entities that share some characteristic or common goal. A huge amount of information about the Brazilian national scientific production can be found in the Lattes Platform, which is a system used to record the curricula of researchers in Brazil. From this information, it is possible to build an academic social network, where relations between researchers represent a partnership in the production of a publication -a link. In social network analysis there is a research area known as link prediction, which aims to identify future relationships. This task may facilitate communication among researchers and optimize the scientific production process identifying possible collaborators. This project analyzed the influence of different attributes found in the literature and data filters to predict co-authorship relationships in academic social networks. Was approached two types of problems in predicting relationships, the general problem that analyzes all possible co-authoring relationships and the problem of new co-authoring that relates to novel co-authorships relationships in the network. The experimental results were promising to the prediction general problem, combining attributes and using filters. However, for the new co-authorships problem the results were not as good. The experiments evaluated different strategies and analyzed the costs and benefits of each. We concluded that to deal with the co-authorships prediction problem in academic social networking it is necessary to analyze the advantages and disadvantages among the strategies, finding a balance between the recall of the positive class and the overall accuracy.

Research universities on the ResearchGate social networking site: An examination of institutional differences, research activity level, and social networks formed

With the development of social networks, we are more interested in every aspect of the relationship. We conducted a visualization analysis of the papers published on two journals of Communication University of China in five years. In addition, we combine the academic social network of three art universities in Beijing based on CNKI and use complex network-related knowledge to analyze the collaboration of these universities. In this paper, we apply the knowledge of visualization tools and complex networks to analyze the authors who have high productivity, the keywords of these papers and the academic partnerships of the three universities. In experiments, each college is regarded as a social network model. From the perspective of academic social networks and collaborative networks, we extract some features of each of the three art universities and make some conclusions.

INTRODUCTION The popularity of academic social networks like ResearchGate and Academia.edu indicates that scholars want to share their work, yet for universities with Open Access (OA) policies, these sites may be competing with institutional repositories (IRs) for content. This article seeks to reveal researcher practices, attitudes, and motivations around uploading their work to ResearchGate and complying with an institutional OA Policy through a study of faculty at the University of Rhode Island (URI). METHODS We conducted a population study to examine the participation by 558 full-time URI faculty members in the OA Policy and ResearchGate followed by a survey of 728 full-time URI faculty members about their participation in the two services. DISCUSSION The majority of URI faculty does not participate in the OA Policy or use ResearchGate. Authors’ primary motivations for participation are sharing their work more broadly and increasing its visibility and impact. Faculty who participate in ResearchGate are more likely to participate in the OA Policy, and vice versa. The fact that the OA Policy targets the author manuscript and not the final published article constitutes a significant barrier to participation. CONCLUSION Librarians should not view academic social networks as a threat to Open Access. Authors’ strong preference for sharing the final, published version of their articles provides support for calls to hasten the transition to a Gold OA publishing system. Misunderstandings about the OA Policy and copyright indicate a need for librarians to conduct greater education and outreach to authors about options for legally sharing articles.

Researchers need to remove many traditional obstacles to disseminate and outreach their research outputs. Academic social networking and blogs allows you to connect with other researchers in your field, share your publications, and get feedback on your non-peer-reviewed work. Blogs are a vital tool for academics to publicly communicate about research developments and findings. The academic social networking, making your work more widely discoverable and easily available. The two best known academic social networking sites are ResearchGate and Academia.edu. These sites offer an instant technique to monitor what other people are looking at in your field of research. Both networks offer roughly the same features. ResearchGate is more closely focused on collaboration and interaction, while Academia.edu often functions more as an academic version of LinkedIn, with an online CV and as a place to share your publications.

Introduction. This research analyses the perception and habits about digital platforms and self-branding among European scholars in the library and information science area. Method. A survey using a questionnaire was administered online to 1,562 scholars working in public universities in the European Union. A response of 201 respondents was obtained. Analysis. The collected data by Google Forms were processed using a specific spreadsheet in Excel and analysed using the software SPSS Statistics. Independent-samples t (two-tailed) and Chi-squared tests were used to check for significant differences between variables. Results. Most participants think that digital platforms are useful for increasing the visibility of their work and managing their reputation, particularly ResearchGate and Google Scholar, although there is limited information in their profiles about their academic activity. The responses show a positive attitude towards personal branding, but most do not adopt it mainly due to not having enough time. Scholars who use personal branding are more committed to the use of personal websites and disseminating their academic achievements. Conclusion. These findings support the need for more in-depth investigations related to the strategies of academics engaging in personal branding, as well as measuring the impact it has on their academic and professional careers.

Dubbed “the world’s first and oldest academic social network” by a grant reviewer at the National Science Foundation, HASTAC (Humanities, Arts, Science, and Technology Alliance and Collaboratory or “Haystack”) built its first interactive website in 2002. Now, 22 years later, HASTAC has some 18,000 network members, over 400 institutional members, and a thriving graduate-student-led HASTAC Scholars program that selects 100 new student members each year. Co-Director Cathy N. Davidson co-founded HASTAC with David Theo Goldberg and numerous other scholars in the humanities and social sciences working in tandem with computer scientists and programmers. Before Wikipedia, Facebook, or Twitter (now X), HASTAC created an open-access, public network with the purpose of making full use of evolving affordances of technology while also critiquing and seeking to improve issues of access, ethics, gender and racial bias, and social and environmental impact. This essay details what it takes to lead and sustain a dues-free, participatory social network with community standards and collaborative decision-making, and where any network member is invited to blog, post, start dialogues, and lead research initiatives, across institutional and other boundaries.

In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students’ unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments.

Several academic social networks have emerged to help researchers who need to search for documents relevant to their interests. The recommendation has been adopted in many websites to suggest relevant documents to users according to their profiles. However, many academic social networks and digital libraries still lack recommendations. In this paper, we propose a new document recommendation approach for the academic social bookmarking website: Bibsonomy. In our method, we use a community detection technique to identify related users. Then, for each target user, the recommended documents are selected from their learning communities. Experimental results show that the proposed method performs better than state-of-the-art recommendation methods.

This paper addresses the author disambiguation problem in academic social network, namely, resolves the phenomenon of synonym problem “multiple names refer to one person” and polysemy problem “one name refers to multiple persons”. A unified semi-supervised framework is proposed to deal with both the synonym and polysemy problems. First, the framework uses semi-supervised approach to solve the cold-start problem in author disambiguation. Second, robust training data generating method based on multi-aspect similarity indicator is used and a way based on support vector machine is employed to model different kinds of feature combinations. Third, a self-taught procedure is proposed to solve ambiguity in coauthor information to boost the performances from other models. The proposed framework is verified on a large-scale real-world dataset, and obtains promising results.

A pervasive trend across academia, social cognition, and general communication contexts is the interchangeable use of “information” and “knowledge”, particularly with reference to their forms—explicit knowledge, testimony, and expertise—conveyed by external sources. This raises a fundamental question: is the source perceived, considered, and validated as a reliable knowledge provider or merely as an information carrier? This study investigates seven academic and popular science sources by modelling their constructs of knowledge provision based on epistemological criteria and sociopsychological value, as manifested through the perspectives of university academics. The external sources examined include scientific journal articles, knowledge shared by university lecturers, scholarly monographs, textbooks and handbooks, popular science books and magazines, academic social networks and social media platforms. A quantitative investigation, supplemented by qualitative content analysis, collected assessments from sixty-six university academics in Latvia using the Epistemological Attitude Questionnaire towards Knowledge Sources. Statistical analysis, coupled with an examination and interpretation of academics’ perceptions, comprehension, use, and personal valuation of these sources, elucidated their profiles. The findings provide a holistic picture of these sources, detailing the value, qualities, functionality, and contributions of each type. Interpretations reveal that the designation of a form of “knowledge source” predominantly aligns with scientific and educational sources, whereas “information carriers” or socially functional sources primarily pertain to popular science and social media. Academic social networks, notably, occupy an intermediary position. This study offers critical academic insights into ongoing issues regarding these means of cognition. It prompts a scrutiny of both established traditional sources and contemporary mediums, both academic and popular, encouraging readers to evaluate these compiled images according to the delineated criteria of the theoretical framework.

Recently, most of the community discovery algorithms are based on the structural information of undirected networks, and the social characteristics of users are less considered. Based on the academic social network, we propose a label propagation algorithm that integrates the network structure and multi-dimensional user information (LPA-NU). Through the fusion of multi-dimensional social networks, the algorithm firstly uses the LDA model to mine the similarity of user research directions to derive the hidden social edges between users. Secondly, it constructs a comprehensive directed weighted network, and then classifies the community according to the initial sub-group information. In order to evaluate the quality of community discovery, this paper proposes the definition of overlapping modules of directed networks. We conduct relevant experiments on real social network datasets (SCHOLAT). Experiments show that the LPA-NU algorithm can better divide the structure of the community, and the quality of community division is higher.