A novel emerging topic detection method: A knowledge ecology perspective

摘要

Emerging topic detection has attracted considerable attention in recent times. While various detection approaches have been proposed in this field, designing a method for accurately detecting emerging topics remains challenging. This paper introduces the perspective of knowledge ecology to the detection of emerging topics and utilizes author-keywords to represent research topics. More precisely, we first improve the novelty metric and recalculate emergence capabilities based on the “ecostate” and “ecorole” attributes of ecological niches. Then, we take the perspective that keywords are analogous to living bodies and map them to the knowledge ecosystem to construct an emerging topics detection method based on ecological niches (ETDEN). Finally, we conduct in-depth comparative experiments to verify the effectiveness and feasibility of ETDEN using data extracted from scientific literature in the ACM Digital Library database. The results demonstrate that the improved novelty indicator helps to differentiate the novelty values of keywords in the same interval. More importantly, ETDEN performs significantly better performance on three terms: the emergence time point and the growth rate of pre-and post-emergence.