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

Jiangen He,Chaomei Chen

doi:10.3389/frma.2018.00009

Abstract

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.

Highlights

Novelty and growth are two widely used attributes for characterizing how a research topic emerges in science (Tu and Seng, 2012; Small et al, 2014; Rotolo et al, 2015)
We described how we decide the window for measuring novelty which can bring most predictive effects on the future growth of scientific knowledge
Before conducting our panel data analysis, we need to identify which window of novelty can produce novelty with strongest predictive effects and the identified novelty metric would be used as independent variable Novelty(t) in our experiment

Summary

Introduction

Novelty and growth are two widely used attributes for characterizing how a research topic emerges in science (Tu and Seng, 2012; Small et al, 2014; Rotolo et al, 2015). Novelty and growth are likely to co-evolve, but evolve along almost inverse paths over different stages of the emergence of a research topic. At the stage right before its emergence, a research topic is characterized by a high level of novelty, but does not attract much attention from the scientific community, and its growth is relatively low due to the limited impact. After acquiring a rapid growth at the stage of emergence, the scientific knowledge of the research topic becomes well established, and the level of novelty is likely to decrease even further at the post-emergence stage. Few studies quantitatively analyzed how novelty affects growth in science

Results

Discussion

Conclusion