  Micro-blogging systems such as Twitter expose digital traces of social
discourse with an unprecedented degree of resolution of individual behaviors.
They offer an opportunity to investigate how a large-scale social system
responds to exogenous or endogenous stimuli, and to disentangle the temporal,
spatial and topical aspects of users' activity. Here we focus on spikes of
collective attention in Twitter, and specifically on peaks in the popularity of
hashtags. Users employ hashtags as a form of social annotation, to define a
shared context for a specific event, topic, or meme. We analyze a large-scale
record of Twitter activity and find that the evolution of hastag popularity
over time defines discrete classes of hashtags. We link these dynamical classes
to the events the hashtags represent and use text mining techniques to provide
a semantic characterization of the hastag classes. Moreover, we track the
propagation of hashtags in the Twitter social network and find that epidemic
spreading plays a minor role in hastag popularity, which is mostly driven by
exogenous factors.
