Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of emerging topics share the similar characters with the cover papers, this study present a novel approach to translate the emerging topics detection to cover paper prediction. By using AdaBoost model and topic model, we construct a machine learning framework to imitate the top journal (chief) editor's judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

Automated summary

This study presents a novel approach to predicting cover papers by translating the detection of emerging topics. By using a machine learning framework and topic model, the judgement of top journal editors in selecting cover papers in the field of material science was imitated, and the results were validated.