Altmetrics与引文指标相关性研究

The Correlation Between Altmetrics and Citations

【目的】研究高质量期刊中论文的Altmetrics指标的相关特性,包括与被引次数相关性、学科差异性、分项指标的贡献度等,对比分析与已有基于全论文数据集分析结果的差异性,为正确理解和使用Altmetrics指标提供借鉴。【方法】选取Nature Index的68种高质量期刊为数据源,利用机器学习方法对论文进行学科分类,采用Spearman相关性分析方法,分析Altmetrics与被引次数之间的相关性及在各个学科中的差别,以及Altmetrics各分项指标的贡献度,并利用ROC曲线评估Altmetrics识别高被引论文的有效性。【结果】Altmetrics与被引次数的相关性存在学科差异;高质量期刊中,论文的Altmetrics分值与被引次数间的相关性增强;News、Blog、Twitter对Altmetrics得分的贡献度增大;Altmetrics有助于识别高被引论文。【局限】所选数据集覆盖年限较短,未进一步根据学科特点扩展数据集。【结论】对比以往全数据集的研究结果,Altmetrics在高质量期刊中的表现具有独特性,Altmetrics与被引次数之间具有强相关性。

[Objective] This paper studies the characteristics of the Altmetrics for high quality journal articles, including their correlations with citation numbers, differences in disciplines, and the contribution of sub-indicators. These Altmetrics are also compared with previous results. [Methods] We selected 68 journals from Nature Index as data sources, and used machine learning method to classify papers published by them. Then, we used Spearman correlation test to find relationship between Altmetrics and traditional citation indexes, as well as the contributions of sub-indicators in various disciplines. Finally, we evaluated the effectiveness of using Altmetrics to identify highly-cited papers, with the help of ROC curve analysis. [Results] There were significant differences in the performance of Altmetrics among disciplines. In high-quality journals, the correlation between Altmetrics and citations were enhanced, and the contributions of News, Blog, and Twitter to the Altmetrics were also increased. Altmetrics could help us identify highly cited papers. [Limitations] The data collection period is short, and the data set needs to be expanded based on the characteristics of the disciplines. [Conclusions] Compared with previous research results of full data sets, Altmetrics for high-quality journal articles are unique, and the correlation between Altmetrics and citations is enhanced.