Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature

摘要

Novel scienti?c knowledge is constantly produced by the scienti?c community. Understanding the level of novelty characterized by scienti?c literature is key for modeling scienti?c dynamics and analyzing the growth mechanisms of scienti?c knowledge. Metrics derived from bibliometrics and citation analysis were effectively used to characterize the novelty in scienti?c 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 scienti?c community over a speci?c 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 scienti?c literature by temporal embedding learning techniques. We validated the effects of the proposed novelty metric on predicting the future growth of scienti?c publications and investigated the relations between novelty and growth by panel data analysis applied in a large-scale publication dataset (MEDLINE/PubMed). Key ?ndings based on the statistical investigation indicate that the novelty metric has signi?cant predictive effects on the growth of scienti?c 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.