More Voices Than Ever? Quantifying Media Bias in Networks

Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Here we propose empirical measures to quantify the extent and dynamics of social (blog) and mainstream (news) media bias. We focus on a particular form of bias—coverage quantity—as applied to stories about the 111th US Congress. We compare observed coverage of Members of Congress against a null model of unbiased coverage, testing for biases with respect to political party, popular front runners, regions of the country, and more. Our measures suggest distinct characteristics in news and blog media. A simple generative model, in agreement with data, reveals differences in the process of coverage selection between the two media. “In the end, we’ll have more voices and more options.” – Dan Gillmor, We the media Introduction Gillmor (2004) envisioned social media, powered by the growth of the Internet and related technologies, as a form of grassroots journalism that blurs the line between producers and consumers and changes how information and opinions are distributed. He argued that “the communication network itself will be a medium for everyone’s voice, not just the few who can afford to buy multimillion-dollar printing presses, launch satellites, or win the government’s permission to squat on the public airways.” This view has been embraced by activists who consider social media as a balancing force to the conventionally assumed slanted or biased elite media. Indeed, social media can be used by underprivileged citizens, promising a profound impact and a healthy democracy. Many believe that the mainstream media is slanted, but disagree about the direction of slant. The conventional belief about media bias has held for decades, but attempts at developing objective measurement have only recently begun. The study by Groseclose and Milyo (2005) showed the presence of bias in mass media (cable and print news) and new media (Internet websites, etc.). Their results, despite receiving criticism, are fairly consistent with conventional wisdom. On the other hand, researchers have observed an “echo chamber” effect within the new media – people select particular news to reinforce their existing beliefs and attitudes. Iyengar and Hahn (2009) argued that such selective exposure is especially likely in the new media environment due to information overload. With search, filtering, and communication technologies, people can easily discover and disseminate information that are supportive or consistent with their existing beliefs. Do social media exhibit more or less bias than mass media and, if so, to what extent? Identifying media bias is challenging for a number of reasons. First, bias is not easy to observe. It has been recognized that “bias is in the eyes of the beholder” meaning that, e.g., conservatives tend to believe that there is a liberal bias in the media while liberals tend to believe there is a conservative bias (Groseclose and Milyo 2005; Yano, Resnik, and Smith 2010). Hence, finding textual indicators of bias is difficult, if not impossible. Second, the assessment of bias usually implies knowing what “fairness” would be, which may not be available or consistent across different viewpoints. Third, Internet-based communication promises easy, inexpensive, and instant information distribution, which not only increases the number of online media outlets, but also the amount and frequency of information and opinions delivered through these outlets. The scale and dynamic nature of today’s communication should be accounted for. In this paper, our major contribution is that we propose empirical measures to quantify the extent and dynamics of “bias” in mainstream and social media (hereafter referred to as News and Blogs, respectively). Our measurements are not normative judgment, but examine bias by looking at the attributes of those being mentioned, against a null model of “unbiased” coverage. Copyright c © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We focus on the number of times a member of the 111th US congress was referenced, and study the distribution and dynamics of the references within a large set of media outlets. We consider “the unbiased” as a configurable baseline distribution and measure how the observed coverage deviates from this baseline, with the measurement uncertainty of observations taken into account. We demonstrate bias measures for slants in favor of specific political parties, popular front-runners, or certain geographical regions. Using these measures to examine newly collected data, we have observed distinct characteristics of how News and Blogs cover the US congress. Our analysis of party and ideological bias indicates that Blogs are not significantly less slanted than News. However, their slant orientations are more sensitive to exogenous factors such as national elections. In addition, blogs’ interests are less concentrated on particular front-runners or regions than news outlets. While our measures are independent of content, we further investigate two aspects of the content related to our measures: the hyperlinks embedded in articles and sentiments detected from the articles. The hyperlink patterns suggest that outlets with a Democrat-slant (D-slant for short) are more likely to cite each other than outlets with a Republican-slant (R-slant). The sentiment analysis suggests there is a weak correlation between negative sentiments and our measures. To better understand the distinctive slant structures between the two media, we propose to use a simple “wealth allotment” model to explain how legislators gain attention (references) from different media. The results about blog media’s inclination to a rich-get-richer mechanism indicates they are more likely to echo what others have mentioned. This observation does not contradict our measures of bias – compared with news media, blogs are weaker adherents to particular parties, front-runners or regions but are more susceptible to the network and exogenous factors. The rest of this paper is organized as follows. We first discuss related work, followed by the details of our collected data. We then detail the different types of coverage bias and how to quantify them and then examine the results, both structurally (via hyperlinking) and textually (via text-based sentiment analysis). Finally, we present a simple generative model of media coverage and conclude with a discussion of open issues and future work.