Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach

Abstract

• We present a novel approach for identifying clusters in a set of citation-based networks to map out interdisciplinary specialty structures. • An ensemble of null network models was adopted to test the (inter)disciplinary composition of bibliometric clusters. • By exploiting an original dataset, we illustrate our approach through the case of an important knowledge domain — social media data-based research. • We add to the literature on the domain of social media data-based research by detecting an increasingly interdisciplinary trend. As science is becoming more interdisciplinary and potentially more data driven over time, it is important to investigate the changing specialty structures and the emerging intellectual patterns of research fields and domains. By employing a clustering-based network approach, we map the contours of a novel interdisciplinary domain – research using social media data – and analyze how the specialty structures and intellectual contributions are organized and evolve. We construct and validate a large-scale (N = 12,732) dataset of research papers using social media data from the Web of Science (WoS) database, complementing it with citation relationships from the Microsoft Academic Graph (MAG) database. We conduct cluster analyses in three types of citation-based empirical networks and compare the observed features with those generated by null network models. Overall, we find three core thematic research subfields – interdisciplinary socio-cultural sciences, health sciences, and geo-informatics – that designate the main epicenter of research interests recognized by this domain itself. Nevertheless, at the global topological level of all networks, we observe an increasingly interdisciplinary trend over the years, fueled by publications not only from core fields such as communication and computer science , but also from a wide variety of fields in the social sciences, natural sciences, and technology. Our results characterize the specialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity.