Identifying and characterizing social media communities: a socio‑semantic network approach to altmetrics
Metadata
Show full item recordEditorial
Springer
Materia
Network analysis Socio-semantic networks Altmetrics Twitter Information science and library science Microbiology
Date
2021-10-12Referencia bibliográfica
Arroyo-Machado, W., Torres-Salinas, D. & Robinson-Garcia, N. Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics. Scientometrics 126, 9267–9289 (2021). [https://doi.org/10.1007/s11192-021-04167-8]
Sponsorship
Spanish Government PID2019-109127RB-I00/SRA/10.13039/501100011033; Spanish Ministry of Universities FPU18/05835; Ramon y Cajal grant from the Spanish Ministry of Science and Innovation REF: RYC2019-027886-I; University of Granada; Universidad de Granada/CBUAAbstract
Altmetric indicators allow exploring and profiling individuals who discuss and share scientific
literature in social media. But it is still a challenge to identify and characterize communities
based on the research topics in which they are interested as social and geographic
proximity also influence interactions. This paper proposes a new method which profiles
social media users based on their interest on research topics using altmetric data. Social
media users are clustered based on the topics related to the research publications they share
in social media. This allows removing linkages which respond to social or personal proximity
and identifying disconnected users who may have similar research interests. We test this
method for users tweeting publications from the fields of Information Science & Library
Science, and Microbiology. We conclude by discussing the potential application of this
method and how it can assist information professionals, policy managers and academics to
understand and identify the main actors discussing research literature in social media.