论文标题
科学影响的早期指标：预测Altmetrics的引用
Early Indicators of Scientific Impact: Predicting Citations with Altmetrics
论文作者
论文摘要
在早期确定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。由于发表的大量研究以及不断变化的跨学科领域的增长，研究人员需要一种有效的方法来识别重要的学术工作。给定的研究出版物的引用数量已用于此目的，但是这些时间需要时间才能增加并积累更长的时间。在本文中，我们使用Altmetrics来预测学术出版物可以收到的短期和长期引用。我们构建各种分类和回归模型，并评估其性能，找到神经网络和合奏模型，以最适合这些任务。我们还发现，Mendeley的读者群是预测早期引用的最重要因素，其次是其他因素，例如读者的学术地位（例如，学生，博士学位，博士学位，教授），Twitter上的关注者，在线邮政长度，作者计数以及Twitter，Wikipedia，Wikipedia和不同国家 /地区的提及数量。
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.
