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.
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.
@cite_5 @cite_29 @cite_14 @cite_21
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It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine learning and crowdsourcing techniques, we investigate the selection and framing of political issues in 15 major U.S. news outlets. Starting with 803,146 news stories published over 12 months, we first used supervised learning algorithms to identify the 14 of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,950 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. We find, however, that news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, we find that major news organizations present topics in a largely non-partisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, there is little evidence of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet's ideological position. Finally, we find that news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences. 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. 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]. 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 sitesin 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 APs top political news sections. We then investigated whether a given story was also chosen to appear on each wires 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.