Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics
作者：
Highlights：
• An algorithmically constructed classification of research publications is created in this study.
• A baseline classification at the level of research topics is used to set the granularity level of the classification.
• The dataset used for the study consists of about 31 million Web of Science publications from 1980 onwards.
• 80% of the articles address topics consisting of 75–716 articles.
• The results of two case studies show that the topics make intuitive sense.
摘要
•An algorithmically constructed classification of research publications is created in this study.•A baseline classification at the level of research topics is used to set the granularity level of the classification.•The dataset used for the study consists of about 31 million Web of Science publications from 1980 onwards.•80% of the articles address topics consisting of 75–716 articles.•The results of two case studies show that the topics make intuitive sense.
论文关键词：Algorithmic classification,Article-level classification,Classification systems,Granularity level,Topic
论文评审过程：Received 27 September 2017, Revised 15 December 2017, Accepted 15 December 2017, Available online 27 December 2017, Version of Record 27 December 2017.
论文官网地址：https://doi.org/10.1016/j.joi.2017.12.006
