  It is part of our daily social-media experience that seemingly ordinary items
(videos, news, publications, etc.) unexpectedly gain an enormous amount of
attention. Here we investigate how unexpected these events are. We propose a
method that, given some information on the items, quantifies the predictability
of events, i.e., the potential of identifying in advance the most successful
items defined as the upper bound for the quality of any prediction based on the
same information. Applying this method to different data, ranging from views in
YouTube videos to posts in Usenet discussion groups, we invariantly find that
the predictability increases for the most extreme events. This indicates that,
despite the inherently stochastic collective dynamics of users, efficient
prediction is possible for the most extreme events.
