In this work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm (2010) elections in the U.S. Our data includes almost 700 candidates and over 690k documents that they produced and cited 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 predicts candidate victory with an accuracy of 88.0%.

SL also makes it easy to The past few years have seen a rise in number and popularity meet and interact with new people. of online spaces where individuals can socialize, play, 4. Transaction: Creating content or providing services in SL and learn. All of these spaces face the challenge of retaining can be profitable, with 150M USD in user-to-user transactions the interest of users over time. We study this problem in taking place in the third quarter of 2009 (Linden the context of Second Life (SL).