The “science-society connect” deals with “transferring the benefits of scientific work to meet existing and emerging societal needs”. In other words, it talks about taking the science to the society so that the society can benefit from scientific research and scientific temper can be inculcated in citizens. The “science-science connect” refers to “sharing of ideas and resources within the knowledge ecosystem”. Though traditionally, science communication happened using journals and conferences but with the penetration of social media, it has grown beyond these boundaries. Therefore, the use of social media for diffusion of scholarly communication into the society has drawn interest across the world. This work presents an exploratory analysis of effectiveness of Twitter as a medium for diffusion of scholarly communication beyond science-science networks.

Collaboration among scientists is always been a fascinating trend to study among researchers. From the level of the institution to national transformed into regional as international collaboration patterns have always the been centre of discussion. Though in the era of globalization and e-mode, collaboration tends to happen ubiquitously but that’s not the case always. Some core countries are always found to be more connected with any other given countries or regions. The core-periphery model has been observed in different research areas or regions. In this thought process, this study explores the probable existence of higher international collaboration over regional collaboration in the case of South Asian countries. Furthermore, it establishes the alignment of South Asian countries towards collaboration with core countries beyond the region.

Articles from multiple research areas tend to use similar words as keywords and titles. On the other hand, selecting a publishing source can be problematic for authors in the initial stage of a scholarly study. So, context-based classification is important where the abstracts, keywords, and titles get similar attention. The aim of this study is to create a tool that uses ensemble learning to classify scholarly articles and recommends sources. This study uses 38 classes for the Web of Science dataset and 40 Classes for the Dimension dataset without grouping them. In all experiment setups models using abstracts achieved the best result as giving a more contextual understanding. Based on this ensemble-based approach, a recommender system has been outlined to recommend probable sources for a given article based on Title, Keywords, and Abstract.