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 .
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 political processes . While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation , the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked . 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. As regards the social analysis , our methodology allows us to extract relevant information about the political orientation 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 different political forces.As regards the semantic analysis, our networks of hashtags display a mesoscale URLanized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment .
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the human behavior
political processes
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group polarization during debates,
elite and mass polarization during online debates and
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and bot detection, the semantic aspect of online communications has received relatively less attention
, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked
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On the `social ' side
As regards the social analysis
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leaning
orientation
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,
-
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,
-
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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
different political forces.As regards the semantic analysis, our
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are characterized by a mesoscale structure which appears to be
display a mesoscale URLanized in a
R
- 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
fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment