Academic Social Networks Research Articles

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The issue of what originality means and how it should be addressed in scientific studies has been a controversial and much disputed subject. It is very difficult to come up with a clear and common definition and conceptualization of originality and to discuss such an intangible subject in the context of all scientific fields. In order for researchers and academics to engage in sustainable R&D activities, originality must be well understood and measurably reflected. This study aims to reveal how the concept of originality has been addressed conceptually in the literature. The keywords related to the subject were filtered with Boolean operators and queried in the academic databases of “Web of Science, Scopus, ScienceDirect, Google Akademik, Microsoft Academic, TR Index” and academic social networks of “Researchgate, Quora, Edigate Insights”. The results show that due to the variation in the topics, approaches and methodologies within different disciplines, the concept of scientific originality differs from one discipline to another. It is very important to understand the difference between the concepts of creativity, novelty and uniqueness, which are closely related to originality. As such, this study offers clues on originality for researchers.

A multi-channel multi-tower GNN model for job transfer prediction based on academic social network

The study of scholar job transfer is beneficial to both individual career success and talent recruitment of institutions. For such a complicated problem, the diversity and objectivity of the obtained information are essentially significant. However, previous works focus on the general job-hopping problems and utilize the information from the recruitment or social platform, which is somehow limited by data privacy. In this paper, we define the scholar job transfer prediction task by introducing more diversified information (e.g., career sequence, collaboration graph), which is obtained from their publications, for more comprehensive modeling without privacy data. Moreover, we design a Multi-channel Multi-tower Enhanced Framework (MMEF) to integrate the heterogeneous inputs in a complementary manner, which can capture the temporal pattern from career trajectories, leverage the academic collaboration information considering the influence from co-authors, and deal with extra descriptions and estimate the relevance scores between the scholars and institutions. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed framework, which outperforms the state-of-the-art approaches by about 20% overall for making better use of the potential patterns in heterogeneous data. More intensive studies explore how and the degree to different collaborators impact scholar job transfer.

A hybrid recommendation system for researchgate academic social network

A hybrid recommendation system for researchgate academic social network

Researchers’ perceptions, patterns, motives, and challenges in self-archiving as a function of the discipline

The green open access (OA) model, which offers the most economical approach to comply with open access policies, can increase researchers’ audience and scientific outputs impact by delivering wider and easier access. This study examined researchers’ perceptions from STEM (science, technology, engineering, math) and SSH (social sciences, art and humanities) disciplines in order to reveal the types, patterns, motives, and challenges underlying their articles’ self-archiving in the green route to open-access (repositories and institutional repositories) and ASNs (academic social networks). Interviews were conducted with 20 Israeli academic researchers. Half were from STEM and half from SSH disciplines. Interviews were mapped using a bottom-up thematic analysis and follow-up quantitative comparisons. According to the findings, STEM researchers self-archived pre/post-print versions of their articles to subject-based repositories as a part of their discipline norm resulting from their funding grant requirements and as a way to receive recognition and claim priority. SSH researchers post a link to the printed-published article at the publisher’s website in ASNs, and their goal is greater visibility. In addition, findings indicate a lack of awareness, mostly by SSH researchers, regarding copyright issues and OA repositories. The green OA model provides opportunities for researchers to self-archive their work. However, there are differences between the disciplines regarding where, when, why, and how to self-archive, and what is considered a legitimate mode of green OA. This indicates an urgent need to raise SSH researchers’ awareness of the existence of open subject-based repositories and of the terms of self-archiving from publishers.

A Personalized Explainable Learner Implicit Friend Recommendation Method

With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challenging task. An important research topic is to accurately discover potential friends of learners to build implicit learning groups and obtain personalized collaborative recommendations of similar learners according to the learning content. This paper proposes a personalized explainable learner implicit friend recommendation method (PELIRM). Methodologically, PELIRM utilizes the learner's multidimensional interaction behavior in social networks to calculate the degrees of trust between learners and applies the three-degree influence theory to mine the implicit friends of learners. The similarity of research interests between learners is calculated by cosine and term frequency–inverse document frequency. To solve the recommendation problem for cold-start learners, the learner's common check-in IP is used to obtain the learner's location information. Finally, the degree of trust, similarity of research interests, and geographic distance between learners are combined as ranking indicators to recommend potential friends for learners and give multiple interpretations of the recommendation results. By verifying and evaluating the proposed method on real data from Scholar.com, the experimental results show that the proposed method is reliable and effective in terms of personalized recommendation and explainability.

The dynamics of Q&A in academic social networking sites: insights from participants, interaction network, response time, and discipline differences

The dynamics of Q&A in academic social networking sites: insights from participants, interaction network, response time, and discipline differences

Academic Social Networking Sites: Evolution, Usage Patterns, and Scholarly Implications

Academic Social Networking Sites: Evolution, Usage Patterns, and Scholarly Implications

Academia Letters: Examination of an ‘Experimental’ Academia.edu Publishing Model

This article makes a historical assessment of a publishing ‘experiment’ that started in 2020 and ended in 2022 by Academia.edu , a popular academic social network site, that took the form of a peer-reviewed ‘journal,’ Academia Letters. The authors discovered a publicly hidden open-access cost, as an article processing charge of US$500, some inconsistencies or ambiguities in select editorial policies, the lack of an editorial board, and the absence of an integrity and publishing ethics policy, cumulatively indicating that this publishing model was lacking some basic, robust scholarly indices that are typically found in conventional peer-reviewed journals. Despite its short two-year history, about 4500 papers were published in Academia Letters, suggesting that this publishing model was nonetheless attractive and popular. This overview of Academia Letters will allow Academia.edu and other academic publishers to reflect on specifics or weaknesses of this publishing model before using it in the future to ensure trustworthy scholarly communication in the academic community.

“Student preference towards the utilization of Edmodo as a learning platform to develop responsible learning environments" study

Teachers can simply establish and administer online learning communities using Edmodo, and students may connect and collaborate with their peers and teachers whenever they want, from any location. A safe and cost-free educational learning network, Edmodo. To promote participation and responsible learning, this study sought to ascertain how much students preferred to utilize the academic social networking site Edmodo. The goal of this essay is to determine whether using Edmodo as a teaching tool is appropriate that more teachers should employ to support a student-centered, technologically-based learning environment where students participate and put their ideas about responsible learning into practice. The study was carried out in a private institution in Banjarharjo, Central Java. Only 320 of the 280 degree-seeking participants were actually used in the analysis. This study used a quantitative methodology,and a survey with research-designed questions was utilized to collectresponds on a Likert scale of 1 to 5, with 1 being strongly agree and 5 being strongly disagree. To encourage students' decision to utilize Edmodo for teaching and learning, the survey question was developed using the RASE (Resources, Activity, Promote, and Evaluation) method.. The study's findings demonstrate that using specific Edmodo features increases student engagement and promotes responsible learning. The analysis's conclusion confirms that the students prefer Edmodo mostly for its resources. assistance and communication tools including forums, chat rooms, and online activities are also available. Students love working on online classes because of Edmodo's great and user-friendly social learning platform. The findings of this study might prompt educators to reconsider how they run their classrooms.

“Student preference towards the utilization of Edmodo as a learning platform to develop responsible learning environments" study

Teachers can simply establish and administer online learning communities using Edmodo, and students may connect and collaborate with their peers and teachers whenever they want, from any location. A safe and cost-free educational learning network, Edmodo. To promote participation and responsible learning, this study sought to ascertain how much students preferred to utilize the academic social networking site Edmodo. The goal of this essay is to determine whether using Edmodo as a teaching tool is appropriate that more teachers should employ to support a student-centered, technologically-based learning environment where students participate and put their ideas about responsible learning into practice. The study was carried out in a private institution in Banjarharjo, Central Java. Only 320 of the 280 degree-seeking participants were actually used in the analysis. This study used a quantitative methodology,and a survey with research-designed questions was utilized to collectresponds on a Likert scale of 1 to 5, with 1 being strongly agree and 5 being strongly disagree. To encourage students' decision to utilize Edmodo for teaching and learning, the survey question was developed using the RASE (Resources, Activity, Promote, and Evaluation) method.. The study's findings demonstrate that using specific Edmodo features increases student engagement and promotes responsible learning. The analysis's conclusion confirms that the students prefer Edmodo mostly for its resources. assistance and communication tools including forums, chat rooms, and online activities are also available. Students love working on online classes because of Edmodo's great and user-friendly social learning platform. The findings of this study might prompt educators to reconsider how they run their classrooms.

Multiplex network community detection algorithm based on motif awareness

Multiplex networks have shown high utility in modeling complex systems, e.g., academic social networks. Composite community structures contribute to revealing meaningful grouping patterns in multiplex networks. Multiplex network community detection, especially using higher-order interaction information, has become a popular research topic in complex network analysis. We propose a multiplex network community detection algorithm based on motif awareness (CDMA) to reduce information loss during the aggregation of multiplex networks, which can solve the community detection problem for cold start nodes and improve the quality of multiplex network community detection. Interlayer differences are fully considered in the topology of the multiplex network throughout the algorithm. First, the motif is used to mine the higher-order interaction information in each layer of the network topology, and a multiplex network aggregation model is constructed to aggregate the multiplex network into a single-layer composite network. In addition, the information propagation method is used to transform the node information in the single-layer composite network into a vector. Furthermore, we propose a node importance evaluation index and select the nodes with the greatest significance to solve the initial node selection sensitivity problem of the k-means algorithm. The comparative experimental results on real multiplex network datasets, e.g., SCHOLAT, show that the proposed CDMA algorithm can play an important role in addressing the issue of effective detection of the community structure of multiplex networks with higher accuracy.

Link Prediction in Co-authorship Networks

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.

Stress, anxiety, and depression in times of COVID-19: Gender, individual quarantine, pandemic duration and employment.

This study explores the inter-relationship between emotional distress in adults and gender, quarantine experiences, pandemic duration, and employment. An online cross-sectional online survey comprised 943 Israelis. The link to the survey was distributed via different personal and academic social networking sites (e.g., Facebook, WhatsApp, and Twitter). The survey was administered using the online survey portal Google Forms. Participants addressed questions about their socio-demographic characteristics (e.g., gender, age, family status, employment, and quarantine experiences) and ranked their levels of stress, anxiety, and depression using the Hebrew version of the Depression, Anxiety and Stress Scale-DASS-21. The majority of the respondents (72%) were women, 39% experienced quarantine, and 55% were unemployed. About 42% experienced a short-term pandemic (one lockdown), and the rest experienced a continuous pandemic (two lockdowns). The MANCOVA results, controlling for family status, indicated that women and unemployed participants reported higher stress, anxiety, and depression levels than men and employed participants. Participants who experienced individual quarantine reported higher anxiety and depression. Furthermore, there was a significant interaction between gender, employment, and pandemic duration. The experience of individual quarantine intensified the stress, anxiety, and depression for both employed and unemployed women. Conversely, the quarantine intensified stress, anxiety, and depression only for unemployed men, whereas the quarantine did not affect stress, anxiety, and depression among employed men. Employment is a critical factor regarding men's emotional state during such stressful situations as the COVID-19 pandemic. Furthermore, individual quarantine and long-term pandemics are associated with opposite outcomes regarding individual mental health. The individual quarantine is associated with increased anxiety and depression, while a long-term, continuous pandemic is associated with decreased stress.

Visual Analysis for Name Disambiguation of Academic Papers

The visual presentation of academic social networks can help users better perform the name disambiguation work in academic paper management. The complex network among authors brings a great challenge to visual interaction. In order to help users better perform the name disambiguation work, a name disambiguation visualization analysis method for academic papers is designed and implemented. Proposed method first generates a cooperative relationship graph based on the cooperative network of co-authors, which is used to reveal the cooperative relationship of authors in the scientific research team. Then, to show the correlation between the research directions of different authors, the visual linkage between the collaboration graph and the published journal graph is designed. Finally, through the combination of the deep learning model, papers and authors are classified respectively to achieve the cross-analysis and coherent reasoning starting from the author or team. The system is based on 4 000 actual papers for case studies and professionals and students are invited to use and evaluate the system, proving the effectiveness in solving name disambiguation.

Investigating users' sustained information seeking on academic social networking sites

PurposeThis study aims to investigate motivators, mediator and moderator of users' sustained information seeking on academic social networking sites (ASNSs).Design/methodology/approachDrawing upon the expectancy–value theory and related information-seeking literature, the study developed a theoretical model to explain why and how users intend to continue seeking information on ASNSs. Thereafter, a field survey with 385 participants was conducted to test the model. Finally, a content analysis of participants' post-survey feedback was performed to complement the model test results by showing more fine-grained findings.FindingsResults suggest that information usefulness and information adoption (IA) are significant to users' sustained information seeking on ASNSs, while users' satisfaction with ASNSs may play a mediating role in the relationship between information usefulness and sustained information seeking. Additionally, self-efficacy for critical thinking (SCT) weakens the impact of IA on users' satisfaction with ASNSs. The post-survey feedback analysis indicates that information usefulness is more critical to sustained information seeking for users with high SCT, whereas IA becomes more crucial to users' satisfaction with ASNSs and sustained information seeking for users with low SCT.Originality/valueAlthough the extant literature has distinguished between information seeking and sustained information seeking, empirical research into users' sustained information seeking on ASNSs is limited. The study fills this gap by proposing and validating relevant factors and the boundary condition of users' sustained information seeking.

Role of Academic Social Networking Sites in Knowledge Sharing and Research Collaboration among Research Scholars

Academic Social Networking Sites (ASNSs) like ResearchGate, Academia.edu, Google Scholar, LinkedIn, etc., are created predominantly for scholars to set up their profiles, upload their scholarly publications, and convey among peers. The prime objectives of the present study are to explore their awareness and use of ASNS amongst the research scholars of science and social science discipline of a prestigious central university in north India, i.e., Aligarh Muslim University (AMU). The survey method was employed, and a simple random sampling technique was used to select the sample by using a structured questionnaire administered to the sample population. Further, it explores its individual use across two dimensions i.e., “Collaborative Research” (CR) and “Information Sharing and Consumption” (ISC) with respect to the gender and discipline. Different hypotheses were framed which signify that there was a significant difference with respect to the gender and discipline across the dimension “CR” with p-value = 0.624 and 0.968, respectively. Moreover, there was again a significant difference with respect to the gender and discipline across the dimension “ISC” with p-value = 0.127 and 0.828, respectively. Further, there is a positive but weak correlation between the constructs CR and ISC, whereas weak to moderate association between their respective items. The implications of the present study would be helpful for regulatory bodies in recognizing the academic activities of the researchers with the use of possible metrics.

The Shady Margins of Open Access: Gray OA and Academic Social Network Sites

ABSTRACT Academic Social Network Sites (ASNS) have grown to become a preeminent location for sharing full text scholarly research articles. This paper examines ASNS within the context of open access by suggesting that the framework of “gray open access” best describes ASNS as sites for the dissemination of published research. To understand why authors share their work on ASNS, this paper reviews library and information science literature, finding trends around desire to expand their works’ reach, appreciation of the ease of using ASNS, lack of knowledge regarding copyright restrictions, and desire to share only the final published version of their work. The paper concludes by suggesting avenues academic libraries can take to address the growth of ASNS as de facto repositories.

Familiarity-Based Collaborative Team Recognition in Academic Social Networks

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution.

Examining social media and academic social network use, and trends in physician-patient communication via social media: a national study

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.

The evolution and co-evolution of a primary care cancer research network: From academic social connection to research collaboration.

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).