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 source ideology. Polarization and bias of media outlets has existed long before the time of internet, however, data availability makes it easier to study and quantify nowadays. @cite_18 study the media habits of American public using a large scale survey and show how fractured the media production and consumption have become in the recent years. @cite_1 propose a method to estimate the ideology of various media sources by comparing the number of citations to think tanks and policy groups to those of Congress members. For a complete survey of methods on measuring ideology of media and media bias, please refer to @cite_20 .
@cite_18 @cite_1 @cite_20
2060704337 2169227786
We measure media bias by estimating ideological scores for several major media outlets. To compute this, we count the times that a particular media outlet cites various think tanks and policy groups, and then compare this with the times that members of Congress cite the same groups. Our results show a strong liberal bias: all of the news outlets we examine, except Fox News' Special Report and the Washington Times, received scores to the left of the average member of Congress. Consistent with claims made by conservative critics, CBS Evening News and the New York Times received scores far to the left of center. The most centrist media outlets were PBS NewsHour, CNN's Newsnight, and ABC's Good Morning America; among print outlets, USA Today was closest to the center. All of our findings refer strictly to news content; that is, we exclude editorials, letters, and the like. "The editors in Los Angeles killed the story. They told Witcover that it didn't ‘come off’ and that it was an ‘opinion’ story.… The solution was simple, they told him. All he had to do was get other people to make the same points and draw the same conclusions and then write the article in their words" (emphasis in original). Timothy Crouse, Boys on the Bus [1973, p. 116]. Partisan bias in the news is a perennial matter of concern and debate for scholars, journalists, politicians, and citizens. Although there are abundant opinions about the magnitude, direction, and even existence of media bias, producing a scholarly consensus on the issue has proven difficult for several reasons. In particular, scholars studying media bias empirically must overcome problems of subjectivity, strategic behavior by the actors involved in the process, and especially the absence of suitable baselines against which to assess bias. This article reviews some approaches to studying media bias and explores several promising strategies and tools scholars have developed to help overcome these obstacles. I conclude with suggestions for future research in the area.