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 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. Media outlets and pundits have been quick to embrace online social networks to disseminate their own opinions. But pundits’ opinions and news coverage are often marked by a clear political bias, as widely evidenced during the fiercely contested 2012 U.S. presidential elections. Given the wide availability of such data from sites like Twitter, a natural question is whether we can quantify the political leanings of media outlets using OSN data. In this work, by drawing a correspondence between tweeting and retweeting behavior, we formulate political leaning estimation as an ill-posed linear inverse problem. The result is a simple and scalable approach that does not require explicit knowledge of the network topology. We evaluate our method with a dataset of 119 million election-related tweets collected from April to November, and use it to study the political leaning of prominent tweeters and media sources. Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three-decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situations. We find that crowdworkers can only achieve an accuracy of 60 in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers’ free responses with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that “context matters”, whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage. News consumers expect news outlets to be objective and balanced in their reports of events and opinions. However, there is a growing body of evidence of bias in the media caused by underlying political and socio-economic viewpoints. Previous studies have tried to classify the partiality of the media, but there is little work on quantifying it, and less still on the nature of this partiality. The vast amount of content published in social media enables us to quantify the inclination of the press to pre-defined sides of the socio-political spectrum. To describe such tendencies, we use tweets to automatically compute a news outlet’s political and socio-economic orientation. Results show that the media have a measurable bias, and illustrate this by showing the favoritism of Chilean media for the ruling political parties in the country. This favoritism becomes clearer as we empirically observe a shift in the position of the mass media when there is a change in government. Even though relative differences in bias between news outlets can be observed, public awareness of the bias of the media landscape as a whole appears to be limited by the political space defined by the news that we receive as a population. We found that the nature of the bias is reflected in the vocabulary used and the entities mentioned by different news outlets. A survey conducted among news consumers confirms that media bias has an impact on the coverage of controversial topics and that this is perceivable by the general audience. Having a more accurate method to measure and characterize media bias will help readers position outlets in the socio-economic landscape, even when a (sometimes opposite) self-declared position is stated. This will empower readers to better reflect on the content provided by their news outlets of choice. 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. For over a generation, social scientists have tried to categorize the relationship between journalists and politicians. Which side holds power and influence over the other? Some scholars propose “active” theories: journalists have preferences and the power to impose them on anyone seeking media attention. Other scholars argue journalists are essentially “reactive,” dutifully writing down what politicians say with little ability to add alternate perspectives. In this dissertation, I propose both camps are extremes based on a fundamental misunderstanding of how journalists can apply their preferences on news content. Politicians and other sources provide information to reporters, bloggers and other new media writers. Each writer then chooses how to respond to this information. Journalistic power – whether we are discussing traditional media outlets or newer partisan media organizations – is best understood as a set of if : then propositions.The empirical sections of the dissertation consist of three separate studies, each of which focuses on one set of inputs and the output from a particular set of news organizations. The first study focuses on how presidents schedule press conferences at particular times and places. I find scheduling influences how much attention journalists give a conference, which in turn influences the balance of opinion found in stories. The second study shows how journalists resist but may ultimately give in to evasive responses, by examining quotations on a statement-by-statement basis. The third study examines some of the most popular phrases from the 2008 election, comparing how a wide range of media organizations responded to the same set of political and non-political ideas. Put together, these studies offer a common theoretical framework for comparing traditional and new media organizations, allowing for commonalities as well as differences. Is the Internet democratizing American politics? Do political Web sites and blogs mobilize inactive citizens and make the public sphere more inclusive? The Myth of Digital Democracy reveals that, contrary to popular belief, the Internet has done little to broaden political discourse but in fact empowers a small set of elites--some new, but most familiar. Matthew Hindman argues that, though hundreds of thousands of Americans blog about politics, blogs receive only a miniscule portion of Web traffic, and most blog readership goes to a handful of mainstream, highly educated professionals. He shows how, despite the wealth of independent Web sites, online news audiences are concentrated on the top twenty outlets, and online organizing and fund-raising are dominated by a few powerful interest groups. Hindman tracks nearly three million Web pages, analyzing how their links are structured, how citizens search for political content, and how leading search engines like Google and Yahoo! funnel traffic to popular outlets. He finds that while the Internet has increased some forms of political participation and transformed the way interest groups and candidates organize, mobilize, and raise funds, elites still strongly shape how political material on the Web is presented and accessed. The Myth of Digital Democracy. debunks popular notions about political discourse in the digital age, revealing how the Internet has neither diminished the audience share of corporate media nor given greater voice to ordinary citizens. We show that the demand for news varies with the perceived affinity of the news organization to the consumer’s political preferences. In an experimental setting, conservatives and Republicans preferred to read news reports attributed to Fox News and to avoid news from CNN and NPR. Democrats and liberals exhibited exactly the opposite syndrome—dividing their attention equally between CNN and NPR, but avoiding Fox News. This pattern of selective exposure based on partisan affinity held not only for news coverage of controversial issues but also for relatively ‘‘soft’’ subjects such as crime and travel. The tendency to select news based on anticipated agreement was also strengthened among more politically engaged partisans. Overall, these results suggest that the further proliferation of new media and enhanced media choices may contribute to the further polarization of the news audience. An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc. This article explains and implements a network analytic approach to the study of cross-platform audience behavior. It begins by conceptualizing large-scale patterns of media use in network terms, treating media outlets as nodes and the levels of audience duplication among them as links. Following that, it explains 2 common measures of audience duplication, Absolute Duplication and Primary Duplication, and offers a new measure, Deviation-from-Random Duplication. In doing so, techniques for converting duplication data into network data are discussed. This approach is then applied to analyze patterns of audience fragmentation, media publics, and audience polarization using data from Nielsen's TV Internet Convergence Panel. The findings show the value of using a network approach, by contributing to an alternative understanding of these patterns. Economic and policy implications are discussed, as well as broader reflections on the use of network analysis in the study of audience behavior. Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93 accuracy in detecting and 89 accuracy in blocking clickbaits. Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long-run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93 accuracy in detecting and 89 accuracy in blocking clickbaits. In a news media environment characterized by abundant choice, it is becoming increasingly easy for Americans to choose news sources slanted toward their own political views rather than sources providing more diverse perspectives. This development poses a challenge to ideals of deliberative democracy if people who consume politically like-minded news disproportionately populate the electoral process, while those presumably reaping the benefits of exposure to more diverse views in the news (e.g., more informed, tolerant attitudes) withdraw from politics. Using panel data collected during the 2008 presidential campaign, this study investigates the proposition that exposure to news slanted toward one's own partisan views increases political participation, while exposure to news with the opposite partisan slant depresses participation. The results suggest that, while exposure to partisan news does not alter the strongly habitual decision to turn out, the hypothesized energizing and enervating effects of exposure do appear for other behavior during the campaign; the partisan hue of the news sources citizens choose to consume affects both when voters decide and their levels of participation over time. We investigate the market for news under two assumptions: that readers hold beliefs which they like to see confirmed, and that newspapers can slant stories toward these beliefs. We show that, on the topics where readers share common beliefs, one should not expect accuracy even from competitive media: competition results in lower prices, but common slanting toward reader biases. On topics where reader beliefs diverge (such as politically divisive issues), however, newspapers segment the market and slant toward extreme positions. Yet in the aggregate, a reader with access to all news sources could get an unbiased perspective. Generally speaking, reader heterogeneity is more important for accuracy in media than competition per se. News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events. While the newspaper industry is in crisis and less time and resources are available for newsgathering, social media turn out to be a convenient and cheap beat for (political) journalism. This article investigates the use of Twitter as a source for newspaper coverage of the 2010 British and Dutch elections. Almost a quarter of the British and nearly half of the Dutch candidates shared their thoughts, visions, and experiences on Twitter. Subsequently, these tweets were increasingly quoted in newspaper coverage. We present a typology of the functions tweets have in news reports: they were either considered newsworthy as such, were a reason for further reporting, or were used to illustrate a broader news story. Consequently, we will show why politicians were successful in producing quotable tweets. While this paper, which is part of a broader project on how journalists (and politicians) use Twitter, focuses upon the coverage of election campaigns, our results indicate a broader trend in journalism. In the future, the reporter who attends events, gathers information face-to-face, and asks critical questions might instead aggregate information online and reproduce it in journalism discourse thereby altering the balance of power between journalists and sources. The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.