  In social network Twitter, users can interact with each other and spread
information via retweets. These millions of interactions may result in media
events whose influence goes beyond Twitter framework. In this paper, we
thoroughly explore interactions to provide a better understanding of the
emergence of certain trends. First, we consider an interaction on Twitter to be
a triplet $(s,a,t)$ meaning that user $s$, called the spreader, has retweeted a
tweet of user $a$, called the author, at time $t$. We model this set of
interactions as a data cube with three dimensions: spreaders, authors and time.
Then, we provide a method which builds different contexts, where a context is a
set of features characterizing the circumstances of an event. Finally, these
contexts allow us to find relevant unexpected behaviors, according to several
dimensions and various perspectives: a user during a given hour which is
abnormal compared to its usual behavior, a relationship between two users which
is abnormal compared to all other relationships, \textit{etc.} We apply our
method to a set of retweets related to the 2017 French presidential election
and show that one can build interesting insights regarding political
organization on Twitter.
