Visual Analytics of Media Frames in Online News and Blogs

Media frames define different perspectives or ways of communicating about issues and can be manifested through patterns of language use such as key words and their composition. Analytically there is interest in trying to identify new frames around issues, and to compare how types of frames vary across different news outlets, or over time. In this paper we consider these analytic needs in the context of two use-cases relating to news producers and news consumers, and describe the initial design of a visual analytics tool, the LingoScope, in terms of how it supports these use-cases. MEDIA FRAMING AND ITS VISUAL ANALYSIS Framing refers to the ways in which an issue is presented in the media, including the various perspectives and conceptions that people communicate with respect to that issue. The way language used around an issue can subtly or not-so-subtly influence the way we think about it. Are you “pro choice”, or “pro life”? With the flip of a single word the abortion issue can be framed with values related to freedom of choice, or with respect to the value of human life – both moral propositions. These kinds of framing effects have implications for public perception and ultimately public opinion on issues of importance to society [2]. If framing is the process, and framing effects are the product, then frames are the objects – the discrete instantiations – of communication, operationalized through the choice and usage of words in conjunction with an issue. In his influential 1993 paper, Entman posits that there are different types of frames that correspond to: the definition of a problem, its causal interpretation, the moral evaluation of the issue, and how the issue or problem might be solved [4]. The issue of illegal immigration, for instance, has frames corresponding to politics, economics, and moral imperatives to name just a few. Each issue would of course have specific linguistic cues associated with each of these frame types. One analytic task relating to frames calls for the use of visualization to support frame reflection, the goal being to identify and surface patterns of language use [1]. The linguistic manifestation of frames including particular keywords, stock phrases, or patterns of word use, suggests an opportunity for visual analytics to identify and help surface these latent linguistic cues. Baumer’s work on this topic [1] seeks to encourage users to consider various linguistic patterns used in the discourse around the issue of cap and trade by visualizing relationships between words. In a study, they found that people who were less concerned with global warming benefitted from the visualization in terms of identifying issue-relevant frames. The identification of frames of interest is one analytic task associated with frame analysis, but an even more prevalent task found in the literature is in differentiating categories of frames and how and with what frequency various communities or media outlets use different frames. Though there is much scholarly research in this domain, one example is that of Grundmann and Krishnamurthy, which looks at comparing media frames on the climate change issue across the US, UK, Germany, and France by assessing the frequency of co-occurrences of certain key terms. They find, for instance, that France and Germany use a predominantly moral frame on the issue, focusing on political perspectives, whereas the US discourse is dominated by a scientific frame [5]. This is a typical analytic task associated with frames in that it seeks to subdivide some corpus (in this case by national outlet) into analytic units and compare the use of frames among those units. The use of visualization to provide better identification and reflection on issue frames in [1] is promising, but at the same time doesn’t address this other analytic need: comparison between sources, or comparison over time for a single or multiple sources. Indeed it’s arguable that visual comparison of texts is poorly supported by existing text visualization techniques such as Wordles [6], Word Trees [7], or even Double Trees [3] and that this needs to be a focus of development in text analytics. In this work we are specifically interested in addressing this issue by developing tools and visualizations to support the visual comparison of frames between different corpora and over time. SPECIFIC USE CASES Our particular use cases combine the notions of frame reflection and identification with that of frame comparison and consider the related needs of (1) news producers and (2) news consumers. The data in these scenarios is multidimensional in the sense of incorporating both the texts themselves (i.e. keyterms and their relationships) as well as a temporal component. The first scenario we consider is that of a journalistic news outlet trying to assess its use and adherence to a style guide. A motivating example from earlier this year is how the Associated Press decided that its style guide should no longer allow for the use of the phrase “illegal immigrant” by reporters. Related idioms like “illegal alien,” “an illegal,” or “undocumented immigrant” were also struck from the guide. Around the same time, the New York Times reported that it is also reconsidering the use of “illegal immigrant”. Other media sources, such as Time, have written about the use of language around gay rights, and of course there are many other examples of the media’s interest and self awareness of language use and codification in style guides. In all of these cases these outlets are clearly reflecting on how sensitive or political issues like immigration are framed in their written output. What can visual analytic tools offer them? What other words besides “undocumented” or “unauthorized” also appear together with the word “immigrant”, and how do such frames around the issue compare over time as the style guide adapts? A tool might help identify new or related frames around immigration to further enable the news outlet to consider its use of those frames. Or it might help a news outlet see to what extent its usage of that frame is adherent to the style guide. This could allow for more awareness of how the editorial voice of the outlet is developing over time. In the news consumption use-case, the analytic tasks become perhaps less about identification of new frames, than about being able to compare frames across different corpora defined by outlet. For instance, as a consumer I may be interested in seeing a comparison of how the Wall Street Journal stacks up against the Washington Post, or the Arizona Republic in the use of the phrase “illegal immigrant”, both in volume and rate of use, as well as how that rate has evolved over time. In essence, visual analytics in this scenario is about empowering the end-user to monitor different media outlets and compare how an issue of interest to them is framed. This could impact their choice of media outlet, or just make them more aware of how a particular outlet’s view or bias on an issue might have 1 http://www.poynter. org/latest-news/mediawire/209045/apchanges-style-on-illegal-immigrant/ 2 http://www.poynter. org/latest-news/top-stories/210053/whatdata-algorithms-teach-us-about-the-language-news-organizationsuse/ 3 http://publiceditor.blogs. nytimes.com/2013/04/02/the-times-toois-reconsidering-the-term-illegal-immigrant/? _r=0 4 http://newsfeed.time. com/2013/03/27/seven-hang-ups-in-thelanguage-of-gay-rights/ changed over time. If an end user is particularly interested in the editorial output and comparison of two outlets, the ability to hone in on sentiment valence, or the types of adjectives could further illuminate the comparison. It should be clear that there is some overlap between these use cases in terms of their emphasis on comparison, and seeing how an outlet (or outlets) have evolved over time. Based on the extant literature on corpus linguistics and frame analysis we see that scholars have similar types of tasks in terms of comparing across and among corpora, as well as identifying new patterns of language use. For our own work, we are additionally interested in comparisons among not just corpora defined by outlet, but also along other dimensions of those outlets such as geography or magnitude of circulation, and among different types of media such as articles vs. comments, articles vs. blogs, and different groupings of blogs based on their network characteristics (such as using community detection algorithms to define subsets of blogs). Beyond the news outlet and news consumer use cases we have defined, we believe there is a rich opportunity to develop tools that also enable scholars to visualize and analyze media frames in many different ways. THE LINGOSCOPE In this section we describe our initial work on designing a system, called the LingoScope, to address the use cases we have identified relating to media frame analysis. The system is shown in Figure 1. The user first selects a news outlet (or two outlets if seeking to compare them), and then enters a search term relating to an issue of concern (e.g., “immigration”). Then the user can define how to operationalize the frame, selecting among contexts defined by words preceding or succeeding the search term, or more loosely defined by words appearing in the same sentence or paragraph. Upon searching, the user will see a list of words, initially ranked by frequency, that correspond to words in the chosen context (in Figure 1 words that come before “immigration”). The user will see a visual comparison of the rate at which each of the corpora uses that word in the given context with the search term. Moreover, the user will be able to see, over time aggregated by month, how the rate of usage has changed and compare between the two corpora. Clicking on the timeline or bar chart allows the user to drill down and see details in the lower right panel, so that they can read through the use of that word in its original context. As the user proceed