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According to the Eurobarometer report about EU media use of May 2018, thenumber of European citizens who consult on-line social networks for accessinginformation is considerably increasing. In this work we analyze approximately$10^6$ tweets exchanged during the last Italian elections. By using anentropy-based null model discounting the activity of the users, we firstidentify potential political alliances within the group of verified accounts:if two verified users are retweeted more than expected by the non-verifiedones, they are likely to be related. Then, we derive the users' affiliation toa coalition measuring the polarization of unverified accounts. Finally, westudy the bipartite directed representation of the tweets and retweets network,in which tweets and users are collected on the two layers. Users with thehighest out-degree identify the most popular ones, whereas highest out-degreeposts are the most "viral". We identify significant content spreaders bystatistically validating the connections that cannot be explained by users'tweeting activity and posts' virality by using an entropy-based null model asbenchmark. The analysis of the directed network of validated retweets revealssignals of the alliances formed after the elections, highlighting commonalitiesof interests before the event of the national elections.

Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections