Identifying important scholarly literature at an early stage is vital to the academic research community and other stakeholders such as technology companies and government bodies. Due to the sheer amount of research published and the growth of ever-changing interdisciplinary areas, researchers need an efficient way to identify important scholarly work. The number of citations a given research publication has accrued has been used for this purpose, but these take time to occur and longer to accumulate. In this article, we use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive. We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks. We also find that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Twitter, Wikipedia, and across different countries.

中文翻译：

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科学影响力的早期指标：使用Altmetrics预测引文

尽早识别重要的学术文献对学术研究界和其他利益相关者（例如技术公司和政府机构）至关重要。由于发表的研究数量之多，以及跨学科领域的发展，研究人员需要一种有效的方法来识别重要的学术著作。为此目的，已经使用了给定研究出版物所产生的引用次数，但是这些引用要花一些时间，而且要积累更长的时间。在本文中，我们使用高度度量法来预测学术出版物可以收到的短期和长期引用。我们建立各种分类和回归模型并评估其性能，找到神经网络和集成模型以最佳地完成这些任务。