  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.
