The midterm (2010) election in the U.S. presented a unique opportunity to study the online social media strategy of various political groups. Although candidates had previously leveraged social media, the prevalence of use during this election allows us to study a significant percentage of candidates and a novel glimpse into their networks and messaging. In combination, the networks and associated content reflect positioning of candidates both structurally and in framing in relation to other politicians. In our work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm elections in the U.S. Our data includes almost 700 candidates and over 460k tweets that they produced in the 3.5 years leading to the elections. We utilize graph and text mining techniques to analyze differences between Democrats, Republicans and Tea Party candidates, and suggest a novel use of language modeling for estimating content cohesiveness. Our findings show significant differences in the usage patterns of social media, and suggest conservative candidates used this medium more effectively, conveying a coherent message and maintaining a dense graph of connections. Despite the lack of party leadership, we find Tea Party members display both structural and language‐based cohesiveness. Finally, we investigate the relation between network structure, content and election results by creating a proof‐of‐concept model that extends incumbency models to predict candidate victory.