People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users' existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem. We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem. Our model incorporates the social-network structure and content-consumption information in a joint factorization problem with shared latent factors. We validate our model and solution on a real-world Twitter dataset consisting of controversial topics, and show that we are able to separate users by ideology with over 90 purity. When applied to media sources, our approach estimates ideology scores that are highly correlated (Pearson correlation 0.9) with ground-truth ideology scores. Finally, we demonstrate the utility of our model in real-world scenarios, by illustrating how the learned ideology latent space can be used to develop exploratory and interactive interfaces that can help users in diffusing their information filter bubble.
I develop a statistical method to measure the ideology of candidates and PACs using contribution data. The method recovers ideal points for incumbents that strongly correlate with ideological measures recovered from voting records, while simultaneously recovering positions for political action committees (PACs), unsuccessful challengers and open-seat candidates. As the candidate ideal points are estimated independently of voting records, they represent a useful new resource for testing models of legislative behavior. By incorporating non-ideological covariates known to influence PAC contributions, the method also shows promise as a platform for furthering our understanding of PAC contribution behavior.  We use bill cosponsorship and roll-call vote data to compare legislators’ revealed preferences in the U.S. House of Representatives and the Argentine Chamber of Deputies. We estimate ideal points from bill cosponsorship data using principal-component analysis on an agreement matrix that included information on all bills introduced in the U.S. House (1973–2000) and Argentine Chamber (1983–2002). The ideal-point estimates of legislators’ revealed preferences based on cosponsorship data strongly correlate with similar estimates derived from roll-call vote data. Also, cosponsorship activity in the U.S. House has lower dimensionality than cosponsorship has in the Argentine Chamber. We explain this lower discrimination as a function of individual- and district-level factors in both countries. The comparative analysis of legislative voting behavior has enjoyed a resurgence of interest in the last decade (Carey 2006; Morgenstern 2004; Sieberer 2006). New statistical techniques and the greater availability of data now allow researchers to map legislative coalitions, explore party discipline, and explain political realignments in multiparty systems (see, for examples, Aleman and Saiegh 2007; Amorim Neto, Cox, and McCubbins 2003; Clinton, Jackman, and Rivers 2004; Desposato 2005; Haspel, Remington, and Smith 1998; Hix, Noury, and Roland 2006; Hug and Schulz 2007; Jones and Hwang 2005a; Londregan 2000; Morgenstern 2004; Poole 2005; and Rosenthal and Voeten 2004). Efforts to understand voting behavior in legislatures across Europe and Latin America not only expand our knowledge about lawmaking and legislative parties, but also promise to shed new light on the forces that shape legislators’ preferences within different institutional contexts.