Early detection of buzzwords based on large-scale time-series analysis of blog entries

In this paper, we discuss a method for early detection of "gradual buzzwords" by analyzing time-series data of blog entries. We observe the process in which certain topics grow to become major buzzwords and determine the key indicators that are necessary for their early detection. From the analysis results based on 81,922,977 blog entries from 3,776,154 blog websites posted in the past two years, we find that as topics grow to become major buzzwords, the percentages of blog entries from the blogger communities closely related to the target buzzword decrease gradually, and the percentages of blog entries from the weakly related blogger communities increase gradually. We then describe a method for early detection of these buzzwords, which is dependent on identifying the blogger communities which are closely related to these buzzwords. Moreover, we verify the effectiveness of the proposed method through experimentation that compares the rankings of several buzzword candidates with a real-life idol group popularity competition.