Knowledge or Information? Shaping Constructs of Academic and Popular Sources

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.

This paper is an overview of the influence of academic social networking sites and management policy on research productivity of library and information science lecturers in Nigerian universities which was anchored on two specific objectives. The paper examined issues relating to the influences of the use of academic social networking sites on research productivity of lecturers identified include: management of an online persona, diffusion of studies, collaboration, information management, and measurement of impact. outcomes of the study showed the influence of management policy on lecturers’ research productivity to thus include: organisation’s research objectives; decentralization; leadership; support for research; policy regimes to motivate lecturers' research activities; and resources. It uses the conceptual and documentary method by using the various related literature to achieve the two stated objectives. Based on the identified factors, the paper recommends the LIS Lecturers commitment to utlisation of academic social networking sites for research visibility and formulation of management timely review of university policies on research productivity of the lecturers among others. The paper conclude that academic social networking sites and university management policies impact research output by LIS lecturers, influencing visibility and encouraging research. And recommends creating social environments that are appropriate for academic purposes networking sites and implementing management policies to make research output more visible. Academic staff should actively utilize academic social networking for research productivity, acquiring necessary ICT and information literacy skills. University administration should also establish guidelines requiring LIS instructors to have profiles on these platforms, benefiting both the university and the instructors.

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.

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.

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.

The paper includes general findings of the study with respect to the impact of academic social networks on the academicians’ professional practice. The authors consider academic social networks as the new means of communication that seek to overcome the limits of traditional means of communication, i.e. academic conferences, scholarly periodicals and books. The study shows that web-platforms, including academic social networks, challenge the superiority of journals in the system of science communications. Based on the results of the empirical study, the authors pay extra focus to studying communication processes via digital platforms. It shows that social networks, due to their specific nature, transform the scientific activity, i.e. change an academician’s motivation and values, encouraging the pursuit of high ratings, more content, citations, followers and page traffic. The authors consider social networks as a tool of open science ideology, concluding that promotion of social networks profiles is underlined by certain power interests aimed at restructuring science communication pursuant to the values of the neoliberal economy.

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.

Despite the proliferation of digital mediums such as documentary series, video essays, and science podcasts, popular science books are still the primary medium for promoting science to the public as an epistemic way to understanding and being aware of our natural world. That said, in the domain of systemic functional linguistics, there exists a dearth of studies investigating popular science books. Hence, this study aims to investigate the organization of popular science discourse from the perspective of Theme Progression. A 93,078-word corpus was collected and divided into two main science categories: hard and soft—three disciplines under each main category and six texts under each discipline. The analysis of the corpus followed a mixed-method design where a Theme-counting excel sheet, created by the researcher, was used to calculate the most occurring Theme patterns. The results of the analysis indicate that the hard science disciplines give a logical presentation of the scientific text that intends to unpack and explain the intricacy behind the scientific notion whereas the soft science disciplines focus on expository narrative to connect and relate the scientific text to the target reader. Given these writing behaviors between the two science categories, it is recommended that future SFL studies explore the potentially arising differences within the broader aspect of the popular science genre, for instance, to juxtapose books from articles in the way discoursal features (e.g., coherence and cohesion) are structured in the text.

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.

The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled their design goal, as scholars' actual online interactions on these platforms remain unexplored. To fill the gap, this article presents a study based on data collected from ResearchGate. Adopting a mixed‐method design by conducting qualitative content analysis and statistical analysis on 1,128 posts collected from ResearchGate Q&A, we examine how scholars exchange information and resources, and how their practices vary across three distinct disciplines: library and information services, history of art, and astrophysics. Our results show that the effect of a questioner's intention (i.e., seeking information or discussion) is greater than disciplinary factors in some circumstances. Across the three disciplines, responses to questions provide various resources, including experts' contact details, citations, links to Wikipedia, images, and so on. We further discuss several implications of the understanding of scholarly information exchange and the design of better academic social networking interfaces, which should stimulate scholarly interactions by minimizing confusion, improving the clarity of questions, and promoting scholarly content management.

Academic social networking sites are becoming increasingly popular. Several groups of academic people at different level of their career and from different disciplines are utilizing them for their benefit. This paper aims to explore usage patterns of an academic Social Networking Site (SNS) (namely Academia.edu) by different groups of academic users. It gains its importance because of the lack of academic social networking sites studies especially those concerning different user types and usage patterns of academic social networking sites.

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.

Recommending conferences for scholars in academic social networks (ASNs) not only can enhance communication between scholars, but also have a power to promote the integration and development of the Internet and other fields. This paper propose to design a neural matrix decomposition model based on scholar influence to recommend conferences in ASNs, we define the scholar influence by measuring the local influence and global influence of scholars on ASNs based on their papers, books and projects, as well as their followers, tweets. We evaluate the effectiveness of our models on the SCHOLAT data set. The promising experimental results demonstrate the effectiveness of the proposed model. KeywordsAcademic social networkAcademic influenceSocial influenceNeural matrix decomposition