TY - JOUR

T1 - Extracting the interdisciplinary specialty structures in social media data-based research

T2 - A clustering-based network approach

AU - Fan, Yangliu

AU - Lehmann, Sune

AU - Blok, Anders

PY - 2022

Y1 - 2022

N2 - 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 net-works, 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 spe-cialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity.

AB - 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 net-works, 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 spe-cialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity.

KW - Bibliometrics

KW - Interdisciplinarity

KW - Social media data

KW - Network science

KW - CITATION

KW - COCITATION

KW - SCIENCE

KW - KNOWLEDGE

KW - DIVERSITY

KW - COHESION

U2 - 10.1016/j.joi.2022.101310

DO - 10.1016/j.joi.2022.101310

M3 - Journal article

VL - 16

JO - Journal of Informetrics

JF - Journal of Informetrics

SN - 1751-1577

IS - 3

M1 - 101310

ER -