The use of citation context to detect the evolution of research topics: a large-scale analysis

被引：18

作者：

Jebari, Chaker ^{[1
]}

Herrera-Viedma, Enrique ^{[2
]}

Jesus Cobo, Manuel ^{[3
]}

机构：

[1] Univ Technol & Appl Sci, Dept Informat Technol, Ibri, Oman

[2] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain

[3] Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain

来源：

SCIENTOMETRICS | 2021年 / 126卷 / 04期

关键词：

Citation context; Research trends; Topic modeling; Biomedical and life sciences;

D O I：

10.1007/s11192-020-03858-y

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

With the exponential increase in the number of published papers, discovering how topics evolve becomes increasingly important for anybody involved in research, including researchers, institutes, research funding bodies, and decision-makers. This study proposes a large-scale analysis of the evolution of biomedical and life sciences using the citation contexts of the collected papers, or more precisely their citing sentences. Using 64,350 papers published in PubMed Central between 2008 and 2018, we determined the research trends for ten research topics. Moreover, we studied how these topics evolve across countries and across the most common journals in biomedical and life sciences.

引用

页码：2971 / 2989

页数：19