text: Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.
  @cite_5: The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91 accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95 accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein.
  @cite_29: Scholars of political communication have long examined newsworthiness by focusing on the news choices of media organizations (Lewin, 1947; White, 1950; Sigal, 1973; Gans, 1979). However, in recent years these traditional arbiters of the news have increasingly been joined or even supplanted in affecting the public agenda by “new media” competitors, including cable news, talk radio, and even amateur bloggers. The standards by which this new class of decision makers evaluates news are at best only partially explained by prior studies focused on professional journalists and organizations. In this study, we seek to correct this oversight by content analyzing five online news sources— including wire services, cable news, and political blog sites—in order to compare their news judgments in the months prior to, and immediately following, the 2006 midterm election. We collected all stories from Reuters’ and AP’s “top political news” sections. We then investigated whether a given story was also chosen to appear on each wire’s top news page (indicating greater perceived newsworthiness than those that were not chosen) and compared the wires’ editorial choices to those of more partisan blogs (from the left: DailyKos.com; from the right: FreeRepublic.com) and cable outlets (FoxNews.com). We find evidence of greater partisan filtering for the latter three Web sources, and relatively greater reliance on traditional newsworthiness criteria for the news wires.
  @cite_14: We study the coverage of U.S. political scandals by U.S. newspapers during the past decade. Using automatic keyword-based searches we collected data on 32 scandals and approximately 200 newspapers. We find that Democratic-leaning newspapers—i.e., those with a higher propensity to endorse Democratic candidates in elections—provide relatively more coverage of scandals involving Republican politicians than scandals involving Democratic politicians, while Republican-leaning newspapers tend to do the opposite. This is true even after controlling for the average partisan leanings of readers. In contrast, newspapers appear to cater to the partisan tastes of readers only for local scandals.
  @cite_21: In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 "A-list" blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.
summary: Selection patterns Several small-scale studies investigate subjects that media outlets select to cover by relying on hand annotated slant labels. For instance, by tracing the media coverage of 32 hand-picked scandal stories, it was shown that outlets with a slant are more likely to cover scandals involving politicians, and vice-versa @cite_14 . Another study @cite_29 focuses on the choices that five online news outlets make with respect to which stories to display in their top news section, and reports that outlets show signs of partisan filtering. In contrast, by relying on an unsupervised methodology, our work explores selection patterns in data involving orders of magnitude more selector agents and items to be selected. Closer to our approach are methods that show political polarization starting from linking patterns in blogs @cite_21 or from the structure of the retweet graph in Twitter @cite_5 . These approaches operate on a predefined - dimension, and assume available political labels. Furthermore, the structure they exploit does not directly apply to the setting of news media articles.