  Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events.
