Using Academic Social Networks to Enhance the Student Experience in Online Education

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.

Graph Embedding for Scholar Recommendation in Academic Social Networks

The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates) and various relationships (e.g., collaboration relationship between team members, citations, and co-authorships), which forms a complex heterogeneous academic network. Our goal is to recommend potential similar scholars for users in ASNs. In this article, we propose to design a graph embedding-based scholar recommendation system by leveraging academic auxiliary information. First, we construct enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by a graph embedding framework, which helps the system measure the similarity between scholars based on their representation vectors. Finally, the system generates potential similar scholars for users in ASNs for the final recommendation. We evaluate the effectiveness of our model on five real-world datasets: SCHOLAT, Zhihu, APS, Yelp and Gowalla. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.

Use of academic social networks by researchers of Sociology in Mexico

Since the 1980s, Mexico has had the National System of Researchers (SNI), a program that recognizes the prestige and quality of the scientific production of the academics that comprise it. Through this, an economic stimulus is granted according to the level obtained, which can be: candidate, level I, II, III, and emeritus. The main of this work is to analyze how the 125 researchers of level III of the area of Sociology have incorporated the use of science 2.0 tools for the visibility and dissemination of their scientific production, from the creation and updating of academic profiles in Google Scholar and on two academic social networks: ResearchGate, and Academia.edu. For this, regardless of the particular indicators of each of these platforms, we review: level of update of the profile; type of documents deposited; number of downloads, cites and visits to their profile.

Bibliometric analysis of document flow on academic social networks in Web of Science

Analysis of a document array on academic social networks (ASNs) in Web of Science for the period from 2005 to 2020 was carried out with use of analytical services data of the WoS and CiteSpace (the program for visualization of patterns and trends in scientific literature). The following parameters of the array were analyzed: publication dynamics; document types structure; countries, organizations and authors leading in the number of publications; thematic categories to which documents of the array are assigned; publications (journals, monographs) in which the documents of the array are published; most cited publications. An increase in the number of publications on the ASNs in WoS was established since 2005. The largest number of ASNs studies is conducted in the USA (University of Pittsburgh), UK (Wolverhampton University, Manchester University), China, Spain (University of Granada), Germany (Max Planck Society for Scientific Research), Canada, India and the Netherlands (Leiden University). The ASNs were studied in the main thematic areas: Computer Science, Computer Science and Librarianship, Mechanical Engineering, Engineering and Technology. Four out of the first ten highly cited publications, are devoted to altmetrics. Using the CiteSpace, it was shown that when ASNs started rise, their organizational structure was beeing studed. Later, altmetrics used in the ASNs became the main subjects of ASNs research. The keywords occurrence revealed that the most frequent terms are “altmetrics”, “impact”, “citation”. As part of the document flow, also identified publications in which the ASNs are used as a source of bibliographic data for systematic or meta-analysis (in medicine predominantly), or as a platform for experimental data discussion.

The Idea and Feasibility of Science Communication in Academic Social Networks

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.