No abstract

Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science (WoS) and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easyto-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.

Based on publications in Chemistry in 2015, this paper investigated the citation impact on domestic and foreign scholarly communities of Chinese and US publications, and to what extent does such impact relate to international collaboration and government funding. First‐ or corresponding authorship in international collaboration is defined as dominance and is taken into consideration. Citations to publications are designated as domestic citations (Chinese or US citations), partner citations or other citations according to overlap of countries of citing and cited publications. Significant difference exists in the two countries. Most citations of Chinese publications are from domestic and partner countries, with domestic citations being the mainstay. In contrast, US papers receive more other citations than domestic or partner citations, and citations from partner countries contribute significantly. US papers have higher global impact than those of China. High domestic citation propensity is associated with non‐international collaboration, dominance of focal country in international collaboration, and national funding support. Such phenomenon is more pronounced in Chinese papers. In other words, citation impact of Chinese publications is more limited in domestic community, whereas that of the US is more global.

Taking Big Data research as a case study, this article intends to investigate the cognitive relatedness of research topics across the global science landscape to a focal topic. Several levels of cognitive relatedness are established depending on the citation distance between the citing publications and a core set of publications. The concept of citation generation is adopted for identifying and classifying other publications with different levels of relatedness to the core set. The micro publication-level classification system of Centre for Science and Technology Studies (CWTS) is applied for determining clusters of publication sets at the topic level. The overall cognitive relatedness of micro clusters to Big Data core publications are measured based on the mean citation generation of all the publications in corresponding clusters. In addition to the given clusters, this study also explores the ‘topics’ relatedness from a semantic point of view, by extracting high-frequency title terms of publications in each generation. Results show that data analysis methods and technologies are the topics with the strongest cognitive relatedness to Big Data research, while topics on physics and astronomy studies present the weakest relatedness. This approach allows assessment of relatedness between research topics by considering the citations distribution across multiple citation generations, and can provide useful insights to study and characterise topics with fuzzy boundaries or are difficult to delineate, thus representing a novel toolset relevant in the context of studying interdisciplinary research.