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www.library.drexel.edu The following item is made available as a courtesy to scholars by the author(s) and Drexel University Library and may contain materials and content, including computer code and tags, artwork, text, graphics, images, and illustrations (Material) which may be protected by copyright law. Unless otherwise noted, the Material is made available for non profit and educational purposes, such as research, teaching and private study. For these limited purposes, you may reproduce (print, download or make copies) the Material without prior permission. All copies must include any copyright notice originally included with the Material. You must seek permission from the authors or copyright owners for all uses that are not allowed by fair use and other provisions of the U.S. Copyright Law. The responsibility for making an independent legal assessment and securing any necessary permission rests with persons desiring to reproduce or use the Material.

Identifying the significance of specific concepts in the diffusion of scientific knowledge is a challenging issue concerning many theoretical and practical areas. We introduce an innovative visual analytic approach to integrate microscopic and macroscopic perspectives of a rapidly growing scientific knowledge domain. Specifically, our approach focuses on statistically unexpected phrases extracted from unstructured text of titles and abstracts at the microscopic level in association with the magnitude and timeliness of their citation impact at the macroscopic level. The H-index, originally defined to measure individual scientists’ productivity in terms of their citation profiles, is extended in two ways: 1) to papers and terms as a means of dividing these items into two groups so as to replace the less optimal threshold-based divisions, and 2) to take into account the timeliness of the impact