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

Autor/inn/en	Wei, Wenjie; Liu, Hongxu; Sun, Zhuanlan
Titel	Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework.
Quelle	In: Scientometrics, (2022) 8, S.4315-4333 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-022-04462-y
Schlagwörter	Cover paper; Emerging topics detection; Research trends prediction; Machine learning; Text mining; Topic model
Abstract	Abstract 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.
Erfasst von	OLC
Update	2023/2/05