Usage of Twitter by politicians has become more prevalent in recent years, with a goal of influencing the electorate and public perception. We collect, explore, and analyze over 12 years of public Twitter interactions of U.S. senators and representatives. Using community detection algorithms on these interaction networks, and without considering the content of the tweets, we are able to infer the political affiliation of each member of Congress with up to 98.8% accuracy in the House and 94.1% accuracy in the Senate. In addition, we define two metrics that can determine the political ideology of members of Congress achieving a very high Spearman’s rank correlation of 0.86 with the existing DW-NOMINATE score from the field of political science. Finally, we expand our structural analysis to intra-party factions and found evidence that some factions act on Twitter more cohesively than others, suggesting an increasing risk of an echo chamber effect when promoting their political agenda.