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.
Perhaps the closest approach to this paper is the work by @cite_13 ( ), who seek to identify the bias of a user on a topic by combining their retweet and content networks, where a content network is obtained based on the similarity of users tweets. , however, assumes the presence of a set of labeled bias anchors (seed hashtags), making it not completely unsupervised. Second, fusing the content and retweet networks is somewhat arbitrary, since there is no common underlying principle that holds the two networks together and hence a graph that results from such a merger contains different types of edges (multigraph) simply merged together. We compared our approach with in and show that our method outperforms their bias scores.
@cite_13
2012923051
We propose a lightweight system for (i) semi-automatically discovering and tracking bias themes associated with opposing sides of a topic; (ii) identifying strong partisans who drive the online discussion; and (iii) inferring the opinion bias of "regular" participants. By taking just two hand-picked seeds to characterize the topic-space (e.g., "pro-choice" and "pro-life") as weak labels, we develop an efficient optimization-based opinion bias propagation method over the social information network. We show how this approach leads to a 20 accuracy improvement versus a next-best alternative for bias estimation, as well as uncovering the opinion leaders and evolving themes associated with these topics. We also demonstrate how the inferred opinion bias can be integrated into user recommendation, leading to a 26 improvement in precision.