Impact of social networks on the dissemination of scientific journals in the Andean Community of Nations (2020-2024)

Academic social networks: Modeling, analysis, mining and applications

PurposeAs an important platform for academic communication and knowledge acquisition, academic social network (ASN) has attracted worldwide researchers. The purpose of this paper is to examine and compare the differences of corporation researchers in ASN utilization from the two aspects of social performance and academic performance.Design/methodology/approachApplying knowledge-based theory, this paper decoupled ASN into social network and academic network and measured utilization of users by social performance and academic performance. Hypotheses were proposed from the perspectives of research areas and corporate reputation. In the part of empirical research, the top 92 research corporations were selected as the sample, and relevant metric data from the member profile pages on ResearchGate was collected for comparing analysis to explore their utilization characteristics.FindingsThe results show that users of different research corporations have certain favoritism in their utilization of ASNs. Science and technology-oriented corporations are better in comprehensive social performance and academic quality. Science-oriented corporations are better at utilizing the interactive functions. However, neither social utilization nor academic utilization, technology-oriented corporations perform well.Originality/valueThis paper focuses on corporation researchers, who have started to embrace ASNs but whose behaviors were less studied. The research paradigm is an expansion and enrichment of the dual network decoupling theory in the field of ASN research. It also deepens the research on ASN utilization of corporation researchers and could give references for ASNs to improve service for corporation users in different research areas.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0389

Academic networks are expected to enhance scientific collaboration and thereby increase research outputs. However, little is known about whether and how the initial steps of getting to know other researchers translates into effective collaborations. In this paper, we investigate the evolution and co-evolution of an academic social network and a collaborative research network (using co-authorship as a proxy measure of the latter), and simultaneously examine the effect of individual researcher characteristics (e.g. gender, seniority or workplace) on their evolving relationships. We used longitudinal data from an international network in primary care cancer research: the CanTest Collaborative (CanTest). Surveys were distributed amongst CanTest researchers to map who knows who (the ‘academic social network’). Co-authorship relations were derived from Scopus (the ‘collaborative network’). Stochastic actor-oriented models were employed to investigate the evolution and co-evolution of both networks. Visualizing the development of the CanTest network revealed that researchers within CanTest get to know each other quickly and also start collaborating over time (evolution of the academic social network and collaborative network respectively). Results point to a stable and solid academic social network that is particularly encouraging towards more junior researchers; yet differing for male and female researchers (the effect of individual researcher characteristics). Moreover, although the academic social network and the research collaborations do not grow at the same pace, the benefit of creating academic social relationships to stimulate effective research collaboration is clearly demonstrated (co-evolution of both networks).

Academic networks are expected to enhance scientific collaboration and thereby increase research outputs. However, little is known about whether and how the initial steps of getting to know other researchers translates into effective collaborations. In this paper, we investigate the evolution and co-evolution of an academic social network and a collaborative research network (using co-authorship as a proxy measure of the latter), and simultaneously examine the effect of individual researcher characteristics (e.g. gender, seniority or workplace) on their evolving relationships. We used longitudinal data from an international network in primary care cancer research: the CanTest Collaborative (CanTest). Surveys were distributed amongst CanTest researchers to map who knows who (the 'academic social network'). Co-authorship relations were derived from Scopus (the 'collaborative network'). Stochastic actor-oriented models were employed to investigate the evolution and co-evolution of both networks. Visualizing the development of the CanTest network revealed that researchers within CanTest get to know each other quickly and also start collaborating over time (evolution of the academic social network and collaborative network respectively). Results point to a stable and solid academic social network that is particularly encouraging towards more junior researchers; yet differing for male and female researchers (the effect of individual researcher characteristics). Moreover, although the academic social network and the research collaborations do not grow at the same pace, the benefit of creating academic social relationships to stimulate effective research collaboration is clearly demonstrated (co-evolution of both networks).

In academic social networks, each user's personal attributes and behaviour preferences form a unique social gene. The recommendation of friends in academic social networks is to use scholars' social genes to recommend scholars who have similar social genes with that scholar. In order to consider the social network structure of users and the attributes of users themselves, SPDNA (Scholar Profile DNA) model is proposed in this paper. The SPDNA model is the user attributes, user preference factor will be extracted according to the DNA model, while the user is formed in the academic social network's influence as the user influence factor, SPDNA factor is used to measure the size of the carrier. The constructed SPDNA model can reflect the personal attributes and behavior preferences of users in academic social networks, and also consider the influence of users in social networks. In the recommended for the user, according to each user to get the SPDNA string matching factor, then the various factors of the SPDNA string similarity calculation summation, then the similarity value obtained in TopK sequencing, end user recommendation set by user influence factor threshold filtering. In addition, through experiments and comparisons, the SPDNA model proposed in this paper also achieves good results in the recommendation of friends, and provides a new solution for the recommendation of friends in academic social networks.

A number of studies have focused on how to constructan academic social network. The relational ties among researchers are either co-authorship or shared keywords that are captured from scientific journals. The problem with such a network is that researchers are limited within their professional social network. In this paper, we propose a novel method for building a social network explicitly based on researchers’ knowledge interests. The researcher’s profile is automatically generated from metadata of scientific publications and homepage. By measuring the similarity between topics of interest, we are able to construct a researcher social network with relational linkages among authors that are produced by matching the their corresponding profiles. A direct loop graph-based social network is proposed. The graph naturally represents such a social network. Interestingly, our results showed that a social network based on profile matching is more representative than network based on publication co-authorship or shared keywords. Researcher mining in the academic social network has been explored via two problems Researcher Ranking and Expert Finding.

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.

PurposeThis study attempts to use a new source of data collection from open government data sets to identify potential academic social networks (ASNs) and defines their collaboration patterns. The purpose of this paper is to propose a direction that may advance our current understanding on how or why ASNs are formed or motivated and influence their research collaboration.Design/methodology/approachThis study first reviews the open data sets in Taiwan, which is ranked as the first state in Global Open Data Index published by Open Knowledge Foundation to select the data sets that expose the government’s R&D activities. Then, based on the theory review of research collaboration, potential ASNs in those data sets are identified and are further generalized as various collaboration patterns. A research collaboration framework is used to present these patterns.FindingsProject-based social networks, learning-based social networks and institution-based social networks are identified and linked to various collaboration patterns. Their collaboration mechanisms, e.g., team composition, motivation, relationship, measurement, and benefit-cost, are also discussed and compared.Originality/valueIn traditional, ASNs have usually been known as co-authorship networks or co-inventorship networks due to the limitation of data collection. This study first identifies some ASNs that may be formed before co-authorship networks or co-inventorship networks are formally built-up, and may influence the outcomes of research collaborations. These information allow researchers to deeply dive into the structure of ASNs and resolve collaboration mechanisms.

Because all disciplines are connected, interdisciplinary studies are one of the most significant discussions in the education sector. It involves the merging of two or more academic disciplines into one activity. The aim of this research paper is to explore the relationship of interdisciplinary research and network among all departments at King Abdulaziz University (KAU) in ResearchGate (RG) by using the statistical network analysis of undirected social networks. In our academic network, the departments of the university represent the vertices and their academic relationships. We will detect the communities between the departments in RG network by using statistical analysis of the network for each community. Finally, we will compare the academic social network at KAU to some random graph models, and investigate some random graph characteristics, such as power-law, small-world, and scale-free models. In our research, we found that the Department of Chemistry has the highest degree for the academic social network at KAU in RG, and the highest eigenvector centrality as well. In terms of vertex centrality, the Department of Electrical and Computer Engineering has the highest value in closeness and betweenness centrality. Also, we found that the most two connected departments are the Department of Computer Science and Department of Physics through the edge weight equals 248. By using community detection, we found there are seven communities. We conclude that the degree distribution of the academic social network of KAU in RG is different from the degree distribution of random graph models, but it is slightly close to small world model. This study , in turn, can participate to achieve one of the goals of Vision 2030 by shedding some light into how to improve research networks in the education sector and research among Saudi universities.

This study investigates to know Myanmar academicians’ use of academic social networks, with especially focus on the Google scholar, Academia.edu and Researchgate. Adopting the descriptive survey design, the study used web based questionnaires as research instrument and were distributed among academicians in Arts and Science Universities in Myanmar. In total, 500 academicians filled in questionnaire were received back. The finding reveals that Myanmar academicians have knowledge of academic social networks. Google scholar is the most common used academic social network among Myanmar academicians. Academicians want to gain more new research area and researchers, but also more communicate with academic social network members for their academic works by joining academic social networks. Furthermore, academicians knew how to use academic social networks for their academic life. Academic social networks are recognized as important one for academicians and also academic social networks should be put into better research tool services.

Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.

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.

In the past few decades, scientific communication has become widespread through academic social networks (ASNs). The purpose of this article is to consider ASNs both as a research tool and as an object of study in scientific publications. The scientometric analysis of literature on ASNs was carried out. The literature set consisted of 1,216 records retrieved from the Web of Science (WoS) database. The set included the documents where the ASNs were the scientific research objects or those where the ASNs were only mentioned as a bibliographic data source for systematic reviews or meta-analyses (predominantly in medicine) or as an experimental data repository. It was found that documents on ASNs started to appear in the WoS after 2005. The USA, the University of Wolverhampton (United Kingdom), and M. Thelwall are the country, organization, and author leading in the number of documents on ASNs in the set. The journals published the documents presented by the following subject areas: Computer Science; Computer Science and Librarianship; Mechanical Engineering; Engineering and Technology. Four out of the first ten highly cited documents are devoted to altmetrics in ASNs. The authors used document co-citation analysis via free scientometric analytical software CiteSpace and discovered research fronts. It was found that when the number of publications on ASNs started to rise, scholars began to discuss the community recommendation, professional indexing/folksonomy, and cold-start problem. Later, the altmetrics used in the ASNs became the main subject in the researches of ASNs. The last statement is confirmed by co-word analysis via CiteSpace. It is shown that the most frequent keywords of the studied document set are altmetrics, impact, and citation.

WhatsApp has become a very useful academic social network where professors can carry out academic work activities such as sharing documents and information, accessing training, coordinating work meetings, monitoring the fulfillment of functions, strengthening personal ties, among others. In this framework, the objective of this study was to determine whether there is a relationship between the use of WhatsApp as an academic social network and the work performance of basic education teachers in the Puno - Peru region in 2025. The study is descriptive and has a correlational cross-sectional design. The population consisted of 3000 teachers of both sexes, with a sample of 341. Data collection was through the questionnaires Use of WhatsApp as an academic social network and Work performance, both with an Aiken V of 0.79, and an internal consistency index α Cronbach's = 0.90 and 0.86 and McDonald's ω = 0.93 and 0.87, respectively. The data were analyzed in the SPSS v.26 statistical program, obtaining a correlation Rho = 0.978 and P-value = 0.000. It is concluded that there is a very high and significant positive correlation between the use of WhatsApp as an academic social network and teacher work performance.

Introduction: In the history of the internet, social media occupy an exceptional place because they bring about sociological changes and cause things that will influence the course of history. It has become inevitable to conduct a study that examines the changes in the relationship between academic social networks and online patient-physician relationships, which have become widespread in recent years, especially among physicians. This study attempted to address this deficiency. Material and Method: An online survey was created on Google Forms that included questions about physicians' use of social and academic media networks and their communication habits with online patients. Age, gender, medical specialty and workplace, social media use, academic social networks usage, and relationships with patients via social media were analyzed. Results: Daily social media usage was significantly associated with age and medical specialty. Participants aged 40-50 and Basic Medic Science Consultants were least likely to use social media. The use of Facebook was the lowest among those under 30 (12.2%). Among those under 30, the use of LinkedIn was deficient (2.0%). Google Scholar was the most frequently used academic social network (38.5%). Surgical specialists were more likely to share medical content. Under 30 and over 50 were more likely to share their medical titles on social media than other groups. The percentage of those who reported having also physically examined the patient during online communication was 64.5%. This high rate is by no means negligible. Patients' most frequent responses to online communication requests were via WhatsApp (80.3%). The under-30 age group was found to have less contact with patients on social media. Conclusion: According to the results of the study, the use of the academic social network is lower than expected, even among academically active participants. The fact that Facebook usage is significantly lower among those under 30 suggests that Facebook is outdated as a social medium for young physicians. Participants in university hospitals, private clinics, and those under 40 use social media differently than other groups. More online patient communication is an important advance. It is also significant that the number of studies has increased after online communication. If investments are made in this topic, it can be expected that a substantial part of patient-doctor relationships will be handled online soon. However, social media studies wear out quickly, so they should be repeated frequently.