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PurposeThrough a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.Design/methodology/approachIn the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.FindingsMost of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.Originality/valueThe findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.

In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network.

In contrast with practice in recent times past, computational and data intensive processes are increasingly driving collaborative research in science and technology. Large amounts of data are being generated in experiments or simulations and these require real-time, or near real-time, analysis and visualisation. The results of these evaluations need to be validated and then published quickly and openly in order to facilitate the overall progress of research on a national and international basis. Research is increasingly undertaken in large teams and is also increasingly interdisciplinary as many of the major research challenges lie at the boundaries between existing disciplines. The move to open access for peer reviewed publications is rapidly becoming a required option in the sector. At the same time, communication and dissemination procedures are also utilising non-traditional forms facilitated by burgeoning developments in social networking. It is proposed that these elements, when combined, constitute a paradigm shift in the model of research and the dissemination of research results.

Many academics are active users of social media and some even use these sites for professional networking. However, while scholars can use traditional social networking platforms to network with their peers, share research articles, and keep up to date in their fields, there are some limitations that emerge when these sites are used for academic purposes. Academic social networking sites have emerged as one viable alternative, as they allow scholars to share their research and to network and collaborate with others while maintaining a professional online presence. Although many studies have examined the information behaviour of those who use academic social networking sites, such as differences in discipline and academic status, no studies to date have explored these characteristics in the health and medical field. This study seeks to address this gap by focusing on the scholarly communication practices of faculty members and graduate students in two disciplines – Medical Sciences and Health Sciences – on Academia.edu.