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Front. Res. Metr. Anal. | doi: 10.3389/frma.2018.00009

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

  • 1Department of Information Science, College of Computing and Informatics, Drexel University, United States

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.

Keywords: Predictive effects, Scientific novelty, Scientific growth, Temporal embedding learning, scientific dynamics

Received: 08 Sep 2017; Accepted: 12 Feb 2018.

Edited by:

Zaida Chinchilla-Rodríguez, Consejo Superior de Investigaciones Científicas (CSIC), Spain

Reviewed by:

Trevor Cohen, University of Texas School of Biomedical Informatics, United States
Kun Lu, University of Oklahoma, United States  

Copyright: © 2018 He and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mr. Jiangen He, Drexel University, Department of Information Science, College of Computing and Informatics, 3141 Chestnut Street, Philadelphia, 19104, Pennsylvania, United States, jiangen.he@drexel.edu
Prof. Chaomei Chen, Drexel University, Department of Information Science, College of Computing and Informatics, 3141 Chestnut Street, Philadelphia, 19104, Pennsylvania, United States, chaomei.chen@drexel.edu