  Large-scale online campaigns, malicious or otherwise, require a significant
degree of coordination among participants, which sparked interest in the study
of coordinated online behavior. State-of-the-art methods for detecting
coordinated behavior perform static analyses, disregarding the temporal
dynamics of coordination. Here, we carry out the first dynamic analysis of
coordinated behavior. To reach our goal we build a multiplex temporal network
and we perform dynamic community detection to identify groups of users that
exhibited coordinated behaviors in time. Thanks to our novel approach we find
that: (i) coordinated communities feature variable degrees of temporal
instability; (ii) dynamic analyses are needed to account for such instability,
and results of static analyses can be unreliable and scarcely representative of
unstable communities; (iii) some users exhibit distinct archetypal behaviors
that have important practical implications; (iv) content and network
characteristics contribute to explaining why users leave and join coordinated
communities. Our results demonstrate the advantages of dynamic analyses and
open up new directions of research on the unfolding of online debates, on the
strategies of coordinated communities, and on the patterns of online influence.
