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.
Estimation of user ideology. Traditionally, the most common sources for estimating ideology comprised of behavioral data generated from roll call votes @cite_3 , co-sponsorship records @cite_8 , or political contributions @cite_12 . These datasets were often only available for the political elite, like members of congress, and hence getting such estimates for a large population of ordinary citizens was difficult, if not impossible.
@cite_12 @cite_3 @cite_8
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We demonstrate that social media data represent a useful resource for testing models of legislative and individual-level political behavior and attitudes. First, we develop a model to estimate the ideology of politicians and their supporters using social media data on individual citizens’ endorsements of political figures. Our measure allows us to place politicians and more than 6 million citizens who are active in social media on the same metric. We validate the ideological estimates that result from the scaling process by showing they correlate highly with existing measures of ideology from Congress, and with individual-level self-reported political views. Finally, we use these measures to study the relationship between ideology and age, social relationships and ideology, and the relationship between friend ideology and turnout. Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classification settings, the training data is divided into a set of unlabeled bags, where only the label distribution in of each bag is known, removing the requirement of instance-level annotations. Our proposed LLP model, Weighted Label Regularization (WLR), provides a scalable generalization of prior work on label regularization to support weights for samples inside bags, which is applicable in this setting where bags are arranged hierarchically (e.g., county-level bags are nested inside of state-level bags). We apply our model to Twitter data collected in the year leading up to the 2016 U.S. presidential election, producing estimates of the relationships among political sentiment and demographics over time and place. We find that our approach closely tracks traditional polling data stratified by demographic category, resulting in error reductions of 28-44 over baseline approaches. We also provide descriptive evaluations showing how the model may be used to estimate interactions among many variables and to identify linguistic temporal variation, capabilities which are typically not feasible using traditional polling methods. Numerous papers have reported great success at inferring the political orientation of Twitter users. This paper has some unfortunate news to deliver: while past work has been sound and often methodologically novel, we have discovered that reported accuracies have been systemically overoptimistic due to the way in which validation datasets have been collected, reporting accuracy levels nearly 30 higher than can be expected in populations of general Twitter users. Using careful and novel data collection and annotation techniques, we collected three different sets of Twitter users, each characterizing a different degree of political engagement on Twitter - from politicians (highly politically vocal) to "normal" users (those who rarely discuss politics). Applying standard techniques for inferring political orientation, we show that methods which previously reported greater than 90 inference accuracy, actually achieve barely 65 accuracy on normal users. We also show that classifiers cannot be used to classify users outside the narrow range of political orientation on which they were trained. While a sobering finding, our results quantify and call attention to overlooked problems in the latent attribute inference literature that, no doubt, extend beyond political orientation inference: the way in which datasets are assembled and the transferability of classifiers. 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.