  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.
