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

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

Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline