Anatomising the impact of ResearchGate followers and followings on influence identification

Abstract

Influence analysis, derived from Social Network Analysis (SNA), is extremely useful in academic literature analytic. Different Academic Social Network Sites (ASNS) have been widely examined for influence analysis in terms of co-authorship and co-citation networks. The impact of other network-based features, such as followers and followings, provided by ASNS such as ResearchGate (RG) and Academia is yet to be anatomised. As proven in ingrained social theories, the followers and followings have significant impact in influence prorogation. This research aims at examining the same in one of the widely adopted ASNS, RG. The rendering process is developed to render real-time RG information, which is modelled into graph. Standard centrality measures are implemented to identify influential users from the constructed RG graph. Each centrality measure gives a list of top- k influential RG users. The results are compared with RGScore and Total Research Interest (TRI) to discover the most effective centrality measure. Betweenness and closeness centrality measures have shown the outperforming results compared with others. A procedure is established to discover influential RG users that are commonly present in all top- k centrality results to identify dominant skills, affiliations, departments and locations from the rendered data.

Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0368-492X.htm community detection and many more (Desai et al, 2022a). Among these applications, influence analysis and recommendation systems are leading.…”

“…An extensive utilization of various scholarly platforms leads to an explosive growth in digital scholarly data. This data plays a vital role in many scholarly applications such as influence analysis, recommendation systems, expert finding, topic modeling, scientific community detection and many more (Desai et al ., 2022a). Among these applications, influence analysis and recommendation systems are leading.…”

“…Influence analysis delineates identifying and ranking dominant scholarly entities such as scholars (Amjad et al ., 2018; Desai et al ., 2019; Wu et al ., 2021), research articles (Savov et al ., 2020), research topics (Liang et al ., 2021), topical experts (Wu et al ., 2021), journals/conferences (Iyengar and Balijepally, 2015), reviewers and research collaborators (Bornmann, 2017; Gasparyan et al ., 2017). Identifying and ranking influential scholars is gaining immense attention recently (Desai et al ., 2022a; Mangan, 2012; Silva et al ., 2021). Traditionally, scholars' publications and citations were used to assess their influence.…”