  On-line social networks publish information on a high volume of real-world
events almost instantly, becoming a primary source for breaking news. Some of
these real-world events can end up having a very strong impact on on-line
social networks. The effect of such events can be analyzed from several
perspectives, one of them being the intensity and characteristics of the
collective activity that it produces in the social platform. We research 5,234
real-world news events encompassing 43 million messages discussed on the
Twitter microblogging service for approximately 1 year. We show empirically
that exogenous news events naturally create collective patterns of bursty
behavior in combination with long periods of inactivity in the network. This
type of behavior agrees with other patterns previously observed in other types
of natural collective phenomena, as well as in individual human communications.
In addition, we propose a methodology to classify news events according to the
different levels of intensity in activity that they produce. In particular, we
analyze the most highly active events and observe a consistent and strikingly
different collective reaction from users when they are exposed to such events.
This reaction is independent of an event's reach and scope. We further observe
that extremely high-activity events have characteristics that are quite
distinguishable at the beginning stages of their outbreak. This allows us to
predict with high precision, the top 8% of events that will have the most
impact in the social network by just using the first 5% of the information of
an event's lifetime evolution. This strongly implies that high-activity events
are naturally prioritized collectively by the social network, engaging users
early on, way before they are brought to the mainstream audience.
