This study investigates how social media shape the networked public sphere and facilitate communication between communities with different political orientations. Using data from Twitter, the research examines two networks of political communication: the retweet network and the mention network. The analysis focuses on over 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. The retweet network is found to have a highly segregated partisan structure, with limited connectivity between left- and right-leaning users. In contrast, the mention network is dominated by a single politically heterogeneous cluster, where users from opposing political views interact more frequently. The study suggests that politically motivated individuals inject partisan content into information streams that primarily target ideologically-opposed users, leading to distinct network topologies. The retweet network exhibits a highly modular structure, segregating users into two homogenous communities corresponding to the political left and right. In contrast, the mention network is more politically heterogeneous, with users of opposing political views interacting at a higher rate. The research also demonstrates that the content of political discourse on Twitter remains highly partisan, with many messages containing extreme sentiments and often disparaging views of users across the partisan divide. The findings suggest that cross-ideological interactions may not significantly reduce political polarization, as users are unlikely to share information from opposing viewpoints with members of their own community. The study concludes that the structure of the retweet network reflects political segregation, while the mention network allows for more diverse interactions. The results highlight the role of hashtags in shaping political communication and the potential for politically motivated content injection to influence network structures. The study provides statistical evidence supporting these findings and emphasizes the importance of understanding the dynamics of political discourse on social media.This study investigates how social media shape the networked public sphere and facilitate communication between communities with different political orientations. Using data from Twitter, the research examines two networks of political communication: the retweet network and the mention network. The analysis focuses on over 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. The retweet network is found to have a highly segregated partisan structure, with limited connectivity between left- and right-leaning users. In contrast, the mention network is dominated by a single politically heterogeneous cluster, where users from opposing political views interact more frequently. The study suggests that politically motivated individuals inject partisan content into information streams that primarily target ideologically-opposed users, leading to distinct network topologies. The retweet network exhibits a highly modular structure, segregating users into two homogenous communities corresponding to the political left and right. In contrast, the mention network is more politically heterogeneous, with users of opposing political views interacting at a higher rate. The research also demonstrates that the content of political discourse on Twitter remains highly partisan, with many messages containing extreme sentiments and often disparaging views of users across the partisan divide. The findings suggest that cross-ideological interactions may not significantly reduce political polarization, as users are unlikely to share information from opposing viewpoints with members of their own community. The study concludes that the structure of the retweet network reflects political segregation, while the mention network allows for more diverse interactions. The results highlight the role of hashtags in shaping political communication and the potential for politically motivated content injection to influence network structures. The study provides statistical evidence supporting these findings and emphasizes the importance of understanding the dynamics of political discourse on social media.