【目的】研究高質量期刊中論文的Altmetrics指標的相關特性,包括與被引次數相關性、學科差異性、分項指標的貢獻度等,對比分析與已有基于全論文數據集分析結果的差異性,為正確理解和使用Altmetrics指標提供借鑒。【方法】選取Nature Index的68種高質量期刊為數據源,利用機器學習方法對論文進行學科分類,采用Spearman相關性分析方法,分析Altmetrics與被引次數之間的相關性及在各個學科中的差別,以及Altmetrics各分項指標的貢獻度,并利用ROC曲線評估Altmetrics識別高被引論文的有效性。【結果】Altmetrics與被引次數的相關性存在學科差異;高質量期刊中,論文的Altmetrics分值與被引次數間的相關性增強;News、Blog、Twitter對Altmetrics得分的貢獻度增大;Altmetrics有助于識別高被引論文。【局限】所選數據集覆蓋年限較短,未進一步根據學科特點擴展數據集。【結論】對比以往全數據集的研究結果,Altmetrics在高質量期刊中的表現具有獨特性,Altmetrics與被引次數之間具有強相關性。

[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.