  The huge amount of data made available by the massive usage of social media
has opened up the unprecedented possibility to carry out a data-driven study of
the human behavior. While particular attention has been paid to phenomena like
group polarization during debates, echo-chambers formation and bot detection,
the semantic aspect of online communications has received relatively less
attention. With the present paper, we carry out a socio-semantic analysis of
the debate about migration policies observed on the Italian Twittersphere,
across the period May-November 2019. On the `social' side, our methodology
allows us to extract relevant information about the political leaning of the
communities of users, hereby called partisan communities, without resorting
upon any external information. Remarkably, our community detection technique is
sensitive enough to clearly highlight the dynamics characterizing the
relationship among the governmental parties: an example is provided by the
effects of the 2019 Italian government crisis on a number of center-left
leaning users - the future members of the Italy Alive party (`Italia Viva') -
whose Twitter activity become markedly different from that of their former
party members - the supporters of the Democratic Party (`Partito Democratico').
On the `semantic' side, instead, the networks of hashtags are characterized by
a mesoscale structure which appears to be core-periphery - a seemingly
recurrent pattern that is present also in the semantic networks characterizing
the online debate in the run up of the 2018 Italian elections.
