[1I2-GS-4a] Webインテリジェンス：モデル化

[1I2-GS-4a-02] Large-scale Analysis of Sleeping Beauty and Its Prince from Scopus

〇Takahiro Miura¹, Kimitaka Asatani¹, Ichiro Sakata¹ (1. School of Engineering, The University of Tokyo)

Keywords:Bibliometrics, Sleeping Beauty, Delayed Recognition, Scopus

With the recent explosive increase in academic papers, researchers effectively understand the previous research by searching the highly cited papers in the related fields. On the other hand, new findings that overturn the concept of a field can be discovered through research that re-evaluates past research, which is different from the trend of the field. In this study, we propose a method to retrieve Sleeping Beauty, which is the paper that became cited after a long sleep, and Prince, which is the paper that triggered the discovery from Scopus whole dataset. The proposed method can exhaustively retrieve Sleeping Beauty and Prince pairs and find that Storyteller papers that cite Sleeping Beauty and Prince simultaneously propagate discontinuous discoveries to the field. Also, we found that discontinuous discoveries were more likely to be due to interdisciplinary research than to random citation though the rarity of the combination between categories had little impact.