Media Content Analysis

Content analysis – “a summarizing, quantitative analysis of messages that relies on the scientific method” to measure frames “as they ‘naturally’ occur” (Neuendorf, 2004: 33) – has been widely employed in framing literature to analyze political elite and media texts (Riffe, Lacy, & Fico, 2014). Like discourse analysis, content analysis generates meaning from text (Weber, 1990: 19), but unlike discourse analysis, it allows the analyst to control contextual factors to, for example, weight identical units of text (e.g.,

SECURITIZATION OF UNAUTHORIZED IMMIGRATION IN THE US

Intertextual Models Official discourse

Number of events Multiple events related by issue Temporal perspective

Comparative moments: 2001-2016 Number of selves

Comparison of two selves across moral evaluation and proposed remedy frame axes

words) equally.²⁴ A constructivist approach to discourse analysis is predicated on the ideal that the analyst will arrive at (nearly) the same subjective interpretation as what naturally occurs among the actors in the case being studied – it is vulnerable to the whims and subjectivities of the analyst. Content analysis, on the other hand, replaces these subjective decisions with defined rules and parameters (Blinder & Allen, 2016). It can be more “accurate” (Balzacq, 2011a: 51) because it is

objective in the sense that the analytic categories are defined so precisely that different coders may apply them and obtain the same results;

systematic in the sense that clear rules are used to include or exclude content or analytic categories; and quantified in the sense that the results of content analysis are amenable to statistical analysis (Hardy et al., 2004:

20)

Quantification and standardization further enables comparisons between actors and across cases/sectors (Blinder & Allen, 2016).²⁵

Content analysis is used in this project to quantify securitizing and desecuritizing frames in primarily media but also political elite discourse. While the discourse analysis guides the content analysis by identifying the key securitizing and

desecuritizing frames and their signifiers, the content analysis charts the prevalence of competing frames over the entire period of analysis, recognizing important periods of coverage rather than singular texts. By quantifying the frames through their signifiers, it is possible to measure competition between opposing frames in each source

(demonstrating, for example, whether and by how much a particular actor favors the desecuritizing or securitizing frame); map the evolution of frame coverage and framing strategies over time; compare frame prevalence across multiple news sources to

identify overlaps and divergence in framing strategies; and compare the press to

24 An advantage of content analysis is its ability to accommodate context as needed through the use of rules. The term “wall”, for example, can refer to border security or finance (Wall Street). A rule can be added that 1) checks for the term “border” within k words of “wall”; 2) ignores collocations of “wall”

and “street”; and 3) requires that the stem “immigr” occurs in the text. Context is thus coded into the content analysis model in a controllable and replicable way (Neuendorf, 2004: 34).

25 Content analysis, in fact, necessitates some comparison – whether it is across time, actors or topics – in order for quantitative data to be useful (Hermann, 2008: 160-161).

political elites in an objective and systematic way. This makes it possible to explicitly answer the primary research question of this thesis and demonstrate whether the press can be an independent securitizing actor. It also addresses the secondary research question, because content analysis makes it possible to quantify the contestation of competing frames.

If content analysis improves upon discourse analysis because it ensures the same results by anyone following the same coding plan, its primary deficit is its inability to capture context and nuance in meaning and understanding of the world as it occurs naturally.

Meanings of texts and words evolve, even in short periods, and content analysis cannot adequately account for these shifts in meaning in language. This limitation is overcome by adopting both methods, drawing on the advantages of each. Discourse analysis provides a holistic understanding of contextual factors that can then be built into the content analysis as rules to ensure only relevant features are quantified.²⁶ The ability to leverage computer assistance, meanwhile, accommodates analysis of much larger volumes of textual data in a consistent and meaningful way (Blinder & Allen, 2016), a necessity for analyzing large sets of text, such as new media. The corpora used in this study cover over 300,000 texts – analyzing these using discourse analysis (or even manual content analysis) has obvious time and resource costs. Human analysis of a smaller corpus is also problematic because it risks an unbalanced analysis, the result of influence and “learning” from previous texts. Computer assisted content analysis ensures consistency and minimizes bias while accommodating large volumes of text data (Baker, 2006; Mautner, 2009).²⁷ News coverage is also more compatible with the aims of content analysis: unlike discourse analysis which uncovers how frames become meaningful, content analysis makes it possible to measure and quantify important attributes of frames (such as strength and magnitude) and explain why they influence

26 This still raises the concern that content analysis reproduces the same subjectivity bias that it seeks to overcome in discourse analysis in the selection of rules and frames to analyze. Some choices are necessary to make in order to guide and narrow the analysis. The content analysis nonetheless improves on discourse analysis by widening the selection of texts analyzed.

27 Roberts et al. (2014) further demonstrate significant overlap and high correlations in findings between automated and hand-coded content analyses, lending confidence in the former as a replacement for the latter.

public preferences (Boomgaarden & Vliegenthart, 2009; Dunaway, Goidel, Kirzinger,

& Wilkinson, 2011; McLaren, Boomgaarden, & Vliegenthart, 2017).

Neuendorf (2004: 35) suggests a sequential strategy in which discourse analysis provides “clues” and coding guidelines that help shape the parameters and boundaries of a content analysis. Despite being more “objective”, content analysis requires subjective decisions to bound and guide analysis – choices must be made “about the limits of what is or is not included in a model or data set” (Herrera & Braumoeller, 2004: 18). Discourse analysis can adequately provide this contextual information (Hermann, 2008). A content analysis on terrorism, for instance, requires delineating the general discourse – e.g., who are the major players, what context of terrorism is

relevant to the study, and in which media/settings are relevant messages on terrorism likely to exist. Discourse analysis can inform the limits of content analysis, while the latter adds replicability and reliability (Balzacq, 2011a: 51; Hardy et al., 2004: 20) through “the assurance that the findings are not entirely the product of one analyst’s opinion” (Neuendorf, 2004: 35). Each can uncover findings not captured in the other (Neuendorf, 2004: 35).

3.3.2.1 Operationalization

The content analysis measures frame competition and source differentiation across four news media sources – CNN, Fox News, MSNBC, the New York Times – and political elites (presidential and congressional discourse). While the analysis of news media introduces to the securitization process four news actors that represent a range of ideology and mediums, the inclusion of political elites serves to both complement the discourse analysis and quantify political discourse so that it is comparable with media analysis. The New York Times is selected because of its strong agenda-setting effect on other news sources (Golan, 2006; Page & Shapiro, 1984; Wanta & Hu, 1993), its demonstrated influence on foreign policy decision-making (Bartels, 1996; Van Belle, 2003), and its wide readership acknowledged in Chapter 1. Wanta and Hu (1993: 255) suggest that even individuals who do not consume it “may have been exposed to other media that had taken salience cues from coverage in the Times.” Its inclusion here offers a chance to test the relationship between a frame’s effectiveness and its source’s credibility (Druckman, 2001).

The three cable news networks accommodate the growing influence of television, which “serves as the key international news source for most Americans” (Golan, 2006:

330; see also Allcott & Gentzkow, 2017). This trend has especially favored CNN, Fox News and MSNBC (Pew Research Center, 2016b), which collectively represent a diverse ideological spectrum and reach wide audiences. Several studies find that Fox News has a conservative and Republican bias; CNN ranges from center to slightly left of center; and MSNBC has a heavy Democratic and liberal gravitation (Aday,

Livingston, & Hebert, 2005; Chalif, 2011; Feldman, Maibach, Roser-Renouf, &

Leiserowitz, 2012). These affiliations are important as the proliferation of news sources and increased partisanship among the electorate has led to targeted programming and consumption (Feldman et al., 2012; McCombs, 2005) where, for example, Fox News targets conservative Republicans (Aday et al., 2005; Gil de Zúñiga, Correa, &

Valenzuela, 2012; Groseclose & Milyo, 2005), while CNN and MSNBC target liberal Democrats (Gil de Zúñiga et al., 2012). Fragmentation determines how audiences calibrate to different sources and the frames they are likely to encounter (Gil de Zúñiga et al., 2012). Moreover, as leading sources of news, these four actors have more

flexibility and autonomy in developing frames (Entman, 2004).

Executive branch texts attributed to the president and vice president as well as

congressional texts are also included to directly compare the press and political elites in compatible terms. These texts are opportunities for political leaders and actors to articulate their policies, influence public expectations and preferences, and shape their personas (Winter, Hermann, Weintraub, & Walker, 1991; Winter & Stewart, 1977).

They also provide raw versions of political elite discourse before it is filtered through media incentives to select what “will sell” (Hermann, 2008: 154). While the discourse analysis primarily focuses on defining moments and symbolic key events (e.g.,

executive orders or presidential vetoes), the content analysis adds a complementary perspective on the everyday minutiae of political rhetoric to show, more granularly, how language evolves, patterns emerge, and trends and shifts occur in political elite texts spanning across presidents and terms. This validates findings from the discourse analysis by linking objective data to interpretations of defining moments, and also facilitates comparisons with news media content, uncovering relationships between both types of actors.

A corpus – or collection of texts – is built for each actor using keyword searches (listed in table 3.1) in LexisNexis Academic (for press actors), the American Presidency Project²⁸ hosted by the University of California at Santa Barbara (for presidents and vice presidents) and the Congressional Record (for congressional actors). The texts include newspaper articles and transcripts of shows for the cable networks; executive branch leaders’ speeches, press releases, public remarks, press briefings, executive orders, interviews, proclamations and other statements; and remarks, hearings, debates and proceedings from Congress. Prior to analysis, all texts are preprocessed using natural language processing tools in Python to extract meta data (e.g., date and source of text) and remove unwanted features (e.g., HTML tags).

Table 3.1 Keyword Search for Content Analysis

Terrorism Unauthorized Immigration

Keywords terrori! OR gwot OR "war on terror" OR

"overseas contingency operation"

migran! OR migrat! OR immigra!

OR refugee! or asyl!

Begin Date Jan 1, 2001 Jan 1, 2001

End Date Dec 31, 2016 Dec 31, 2016

Following previous framing scholarship (e.g., Bennett et al., 2006; Rowling et al., 2011;

Simon & Jerit, 2007), frames are identified based on the presence of signifiers.

Signifiers can be words (e.g., immigrant), word stems (e.g., the feature immigr includes instances of immigration, immigrant(s), and immigrate(d/s)), sequences of words (e.g., illegal immigrant), more complex combinations of words like collocations (e.g., wall or fence occurring within five words of border; see also Blinder and Allen, 2016), or some combination of these rules. Because journalists adopt similar ways of presenting topics – or “conventional frames” (Norris, Kern, & Just, 2004; Pan & Kosicki, 1993)²⁹ – the same signifiers are used for each actor. The selected signifiers employed in this research

28 https://www.presidency.ucsb.edu/

29 Political elites face similar constraints: Hansen (2013: 6-7) argues that it would be “extremely unlikely—and politically unsavvy—for politicians to articulate foreign policy without any concern for the representations found within the wider public sphere as they attempt to present their policies as legitimate to their constituencies.”

are specific enough to capture the construct that the content analysis aims to measure, while also remaining broad enough to prevent overfitting. Overfitting risks understating the presence of frames, and not fully accommodating the nuanced ways in which

different actors/sources invoke “conventional frames”. The signifiers are thus defined in this research using minimal keywords and restrictions.³⁰ This choice follows precedents set by other scholars who demonstrate framing effects resulting from the substitution of a few keywords (e.g., Bennett et al., 2006; Kahneman & Tversky, 1984; Simon & Jerit, 2007).

Both frames and their primary signifiers are identified in the discourse analysis.

Specifically, the discourse analysis focuses on rhetorical links and differentiation between competing (de)securitizing basic discourses. These rhetorical links are used in the content analysis as signifiers to identify frames used in the securitizing and

desecuritizing discourses. Multiple frames may constitute a securitizing basic discourse: in the immigration debate, for example, securitizing discourses include moral evaluation frames (immigrants as criminals and threats) and remedy proposal frames (increased border security). Desecuritizing discourses, conversely, move away from security and employ frames that provide different moral evaluations (immigrants as victims) and remedy proposals (pathways toward legalization and citizenship). The discourse analysis for each case study drives signifier selection, and the content analysis uses these signifiers to measure frame prevalence.

Frame competition is measured by comparing the magnitude of opposing securitizing frames and desecuritizing frames. Given wide scholarly agreement that frame

magnitude is the most important predictor of framing effects, it is the preferred

mechanism here for measuring competition between (de)securitization frames. Guided by this consensus and extant research (Bennett et al., 2006; Rowling et al., 2011; Simon

& Jerit, 2007), magnitude is measured as the number of occurrences of each signifier.

For example, the magnitude of the border security frame in a single document could be

30 The content analysis also does not discriminate between foreign policy, arts, sports, or style sections.

This choice reflects the assumption that audience knowledge is constructed from different sources and settings (Blinder & Allen, 2016: 9). In expressing their policy preferences, they are likely to draw on whichever nodes are accessible (Iyengar & Kinder, 1987; see also Domke et al., 1998; Higgins & King, 1981), rather than deliberating on the literary, foreign policy or security connotations of, for example, torture and immigrants.

the number of instances of “border wall”, “border fence” or “border security” that occur in the text. In order to control for varying sizes of texts – television show transcripts, for example, are much longer than news articles – frame magnitudes are standardized to rates of the frame occurrence per ten-thousand words. Both aggregate and quarterly time series measures of magnitude are constructed for each actor and each frame.³¹ These are converted into a single construct (e.g., a ratio of

securitizing-to-desecuritizing frame magnitudes) to measure frame competition. Both trends as well as aggregate measures of frame magnitudes are observed to assess differences in actor coverage of securitizing and desecuritizing frames (source differentiation), paving the way for analysis of how this relates to audience preferences. Importantly, the emphasis is on relative measures rather than absolute measures. In other words, the prevalence of frames is compared among actors, rather than specifying an arbitrary baseline against which to draw inferences (Hofstetter, 1976).