Buzzword detection in the scientific scenario

This paper addresses a relatively new concept: the buzzword. Buzzwords are fashionable words that continue gaining popularity until a tipping point is reached and then their popularity declines. Our goal in this study is to identify buzzwords through their frequency of occurrence over the years, using two clustering techniques: k-means and the self-organizing map (SOM). We also used the DBLP database to run experiments with data from published papers in an attempt to find terms that could be classified as buzzwords, in accordance with the defined meaning. Clusters generated by both k-means and SOM are very similar, indicating that it is very likely that buzzwords were correctly identified as such. We were able to identify terms such as “android” and “mapreduce”, which were clearly buzzwords for 2012, as well as terms such as “pomdp”, which was not an obvious buzzword. As a contribution, we highlight common characteristics identified for buzzwords and make comparisons between the two methods for finding buzzwords which were analyzed in this paper.