  Detecting and characterizing emerging topics of discussion and consumer
trends through analysis of Internet data is of great interest to businesses.
This paper considers the problem of monitoring the Web to spot emerging memes -
distinctive phrases which act as "tracers" for topics - as a means of early
detection of new topics and trends. We present a novel methodology for
predicting which memes will propagate widely, appearing in hundreds or
thousands of blog posts, and which will not, thereby enabling discovery of
significant topics. We begin by identifying measurables which should be
predictive of meme success. Interestingly, these metrics are not those
traditionally used for such prediction but instead are subtle measures of meme
dynamics. These metrics form the basis for learning a classifier which
predicts, for a given meme, whether or not it will propagate widely. The
utility of the prediction methodology is demonstrated through analysis of memes
that emerged online during the second half of 2008.
