Early indicators of scientific impact: Predicting citations with altmetrics

Abstract 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.