Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature - He, Jiangen; Chen, Chaomei (Frontiers in Research Metrics and Analytics, 2018)
Novel scientiﬁc knowledge is constantly produced by the scientiﬁc community. Understanding the level of novelty characterized by scientiﬁc literature is key for modeling scientiﬁc dynamics and analyzing the growth mechanisms of scientiﬁc knowledge. Metrics derived from bibliometrics and citation analysis were effectively used to characterize the novelty in scientiﬁc development. However, time is required before we can observe links between documents such as citation links or patterns derived from the links, which makes these techniques more effective for retrospective analysis than predictive analysis. In this study, we present a new approach to measuring the novelty of a research topic in a scientiﬁc community over a speciﬁc period by tracking semantic changes of the terms and characterizing the research topic in their usage context. The semantic changes are derived from the text data of scientiﬁc literature by temporal embedding learning techniques. We validated the effects of the proposed novelty metric on predicting the future growth of scientiﬁc publications and investigated the relations between novelty and growth by panel data analysis applied in a large-scale publication dataset (MEDLINE/PubMed). Key ﬁndings based on the statistical investigation indicate that the novelty metric has signiﬁcant predictive effects on the growth of scientiﬁc literature and the predictive effects may last for more than ten years. We demonstrated the effectiveness and practical implications of the novelty metric in three case studies.