Social media play a pivotal role in shaping citizens  political opinion. According to the Euro-barometer , the percentage of EU citizens employing online social networks to access information, on a daily basis , has increased from 18\% in 2010 to 42\% in 2017. The tight entwinement between social media and the unfolding of political dynamics has motivated the interest of researchers for the analysis of users online behavior - with particular emphasis on topics like group polarization during debates and echo-chambers formation - to unveil the modes and the implications of online interactions for political processes . In this context, where attention has gone predominantly towards the study of online relations between users , semantic aspects have remained under-explored. In the present paper, we aim at filling this gap by , first, identifying the discursive communities that animate the political debate in the run up of the 2018 Italian Elections and, then, studying the semantic mechanisms that shape their internal Twitter discussions . We do so by monitoring, on a daily basis, the structural evolution of the corresponding semantic networks . As our analysis points out, the supporters of the political alliances present at the elections are characterized by a markedly different online behavior, in turn inducing semantic networks with different topological structures. The supporters of the right-wing parties alliance display a particularly active behavior condensed in a single, densely connected cluster wherein discussions take place in conjunction with mediated events such as political talk shows. Daily semantic networks triggered by the usersretweeting members of the 5 Star Movement (M5S)tend, instead, to be less centralized suggesting a "more distributed" way of discussing a variety of themes, e.g. those raised as central by this new incumbent in the Italian political scenario .
Social media play a key role in shaping citizens ' political opinion. According to the Eurobarometer , the percentage of EU citizens employing online social networks on a daily basis has increased from 18\% in 2010 to 48\% in 2019. The entwinement between social media and the unfolding of political dynamics has motivated the interest of researchers for the analysis of users online behavior - with particular emphasis on group polarization during debates and echo-chambers formation . In this context, attention has been predominantly directed towards the study of online relations between users while semantic aspects have remained under-explored. In the present paper, we aim at filling this gap by adopting a two-steps approach. First, we identify the discursive communities animating the political debate in the run up of the 2018 Italian Elections as groups of users with a significantly-similar retweeting behavior. Second, we study the semantic mechanisms that shape their internal discussions by monitoring, on a daily basis, the structural evolution of the semantic networks they induce. Above and beyond specifying the semantic peculiarities of the Italian electoral competition, our approach innovates studies of online political discussions in two main ways. On the one hand, it grounds semantic analysis within users' behaviors by implementing a method, rooted in statistical theory, that guarantees that our inference of socio-semantic structures is not biased by any unsupported assumption about missing information; on the other, it is completely automated as it does not rest upon any manual labelling (either based on the users' features or on their sharing patterns). These elements make our method applicable to any Twitter discussion regardless of the language or the topic addressed .
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pivotal
key
A
'
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Euro-barometer
Eurobarometer
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to access information,
D
,
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42\% in 2017. The tight
48\% in 2019. The
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topics like group polarization
group polarization
D
- to unveil the modes and the implications of online interactions for political processes
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where attention has gone predominantly
attention has been predominantly directed
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,
while
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, first, identifying
adopting a two-steps approach. First, we identify
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that animate
animating
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and, then, studying
as groups of users with a significantly-similar retweeting behavior. Second, we study
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Twitter discussions . We do so
discussions
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corresponding semantic networks . As our analysis points out, the supporters of the political alliances present at the elections are characterized by a markedly different online behavior, in turn inducing semantic networks with different topological structures. The supporters of
semantic networks they induce. Above and beyond specifying the semantic peculiarities of the Italian electoral competition, our approach innovates studies of online political discussions in two main ways. On the one hand, it grounds semantic analysis within users' behaviors by implementing a method, rooted in statistical theory, that guarantees that our inference of socio-semantic structures is not biased by any unsupported assumption about missing information; on the other, it is completely automated as it does not rest upon any manual labelling (either based on the users' features or on their sharing patterns). These elements make our method applicable to any Twitter discussion regardless of
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right-wing parties alliance display a particularly active behavior condensed in a single, densely connected cluster wherein discussions take place in conjunction with mediated events such as political talk shows. Daily semantic networks triggered by the usersretweeting members of the 5 Star Movement (M5S)tend, instead, to be less centralized suggesting a "more distributed" way of discussing a variety of themes, e.g. those raised as central by this new incumbent in the Italian political scenario
language or the topic addressed