%A Wu Pengmin,Chen Ting,Wang Xiaomei %T The Correlation Between Altmetrics and Citations %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.0354 %P 58-69 %V 2 %N 6 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4519.shtml} %8 2018-06-25 %X

[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.