Acceso usuarios
Henry Small, Kevin W. Boyack, Richard Klavans
The identification of emerging topics is of current interest to decision makers in both government and industry. Although many case studies present retrospective analyses of emerging topics, few studies actually nominate emerging topics for consideration by decision makers. We present a novel approach to identifying emerging topics in science and technology. Two large scale models of the scientific literature, one based on direct citation, and the other based on co-citation, are combined to nominate emerging topics using a difference function that rewards clusters that are new and growing rapidly. The top 25 emergent topics are identified for each year 2007 through 2010. These topics are classified and characterized in various ways in order to understand the motive forces behind their emergence, whether scientific discovery, technological innovation, or exogenous events. Topics are evaluated by searching for recent major awards associated with the topic or its key researchers. The evidence presented suggests that the methodology nominates a viable list of emerging topics suitable for inspection by decision makers.
