Audience Evaluation

Public opinion data has been underutilized in securitization research, but regular surveys and polls conducted by news organizations, governments, universities, think tanks, and polling agencies enable measuring public preferences for policies – such as exceptional measures – both cross-sectionally and longitudinally. These instruments are useful because of their “‘objective’ and seemingly decisive nature, as well as their ability to account for a multitude of individual opinions” while remaining

“unprejudiced by ideology” to “communicate the general will” (Herbst, 1993: 2).

Conducted on representative samples, public opinion surveys and polls accommodate geographically large and ideologically diverse audiences. By quantifying audience sentiment, it becomes possible to determine the extent of public support for exceptional measures across different security issues at different times, directly satisfying the secondary research question of this thesis.³² Identifying audience acceptance/rejection

31 The use of time series to measure media effects has been used widely in framing literature to

demonstrate interactions between the media and political agenda (Bartels, 1996; Baumgartner, Jones, &

Leech, 1997; Box-Steffensmeier, Darmofal, & Farrell, 2009; Fogarty & Monogan III, 2016; Kellstedt, 2000; Simon & Jerit, 2007; Soroka, 2002; Vonbun, Königslöw, & Schoenbach, 2016; Walgrave, Soroka,

& Nuytemans, 2008; Wood & Peake, 1998). The current study shares these previous studies’ objective of quantifying the rhetoric of different actors and evaluating potential directions of influence.

32 Balzacq (2011a) cautions that polls should be used in securitization theory strictly as indicators of the prominence of an issue – and not as evidence of securitization – because of their dual nature as effects in

of exceptional measures is also necessary for answering the primary research question – whether the press can be an independent securitizing actor – because a link needs to be made between press use of frames and audience attitudes. The use of public opinion polling and survey data makes it possible to directly measure whether audiences support or reject exceptional measures.

Several studies outside of securitization have used polling and survey data to relate the public and other actors: Zaller (1992) shows that elite discourses shape mass opinion;

Groeling and Baum (2008) use content analysis to show that elite rhetoric shapes public opinion on foreign policy and military issues; Berinsky (2007: 975) finds that “patterns of conflict among partisan political actors shape mass opinion on war”; and other studies (Hertog & Fan, 1995; Page et al., 1987) find that news media can influence public attitudes. These studies employ different methods toward different agendas, but they share a reliance on polls as indicators of public opinion on policy issues (though largely non-security ones) and the assumption that framing effects are top-down. In other words, political elites and the media influence the public rather than the other way around. This direction is cautiously assumed in the case studies in this research as

and effects of securitization: “the results of polls can be instrumentalized and play a role in securitizing moves, but can also be utilized to account for (successful) cases of securitization” (42). On the one hand, public opinion is influenced by policy proposals and is a result of the securitizing move (effect of); on the other hand, securitizing actors can adjust policy to synchronize with public preferences (Campbell, 2012;

Jacobs & Shapiro, 2002) – the effect in. Balzacq’s concern, then, is that using public opinion to measure the effect of discounts the possibility that public opinion may also be an effect in. This is especially problematic if the poll is only an effect in and never an effect of previous securitizing discourse. To mitigate this concern, the analyses in the case studies use several polls, recognizing that some may drive the effect in, but that those follow securitizing moves necessarily reflect the effect of (even though they may also be “instrumentalized” by securitizing actors for future securitizing moves).

The problem is further mitigated by focusing on polls that measure audience evaluation of the specific exceptional measure proposed. These specific measures are only going to be the subject of public opinion inquiry after they have been proposed as potential policy options – in other words, the

securitization move has already occurred. It is by virtue of being “newsworthy” or part of public debate that an issue warrants public opinion inquiry; it is unlikely to find a poll that queries the public’s concern for a threat (or support for exceptional measures to reduce the threat) before the threat has even

materialized (or the measures have even been proposed).

well³³ – specifically this project applies methods used in past research to measure audience acceptance or rejection of an exceptional measure, the level or extent of acceptance/rejection, and temporal shifts in these levels. In doing so, the following questions are addressed:

1) Does the audience accept or reject the exceptional measure?

2) How much does the audience accept or reject the exceptional measure?

3) Has audience acceptance/rejection increased or decreased over time?

These questions are answered using longitudinal poll and cross-sectional survey data to holistically analyze the audience in securitization. This approach to audience evaluation can accommodate large audience sizes and variability in parsimonious representations and models. While discourse analysis risks reducing the securitization audience to an invariable, monolithic and overgeneralized entity, longitudinal and cross-sectional models introduce variance and representative samples of the audience. Quantified data also make it possible to compare effects of different variables both within and across cases, because audience acceptance or rejection of securitization can be measured with precision in replicable ways. To that end, the macro-level longitudinal analysis charts audience evaluation over time, showing how public responses evolve during defining and low-salience moments. Using data from identically or similarly worded question-response pairs,³⁴ both audience threat anxiety and support/opposition for various

33 Certainly public opinion influences policy outcomes, especially on highly salient issues (Campbell, 2012). The literature widely acknowledges that public opinion influences policy (e.g., Aldrich, Gelpi, Feaver, Reifler, & Sharp, 2006; Burstein, 2003; Hartley & Russett, 1992; Smith, 1999); that policy outcomes affect public opinion (e.g., Stimson, 2015; Wlezien, 1995); that policy outcomes and public opinion are reciprocally linked (e.g., Erikson, Wright, & McIver, 1993; Hill & Hinton-Anderson, 1995;

Monroe, 1998; Page, 1994; Page & Shapiro, 1983, 2010; Stimson, MacKuen, & Erikson, 1995; Wlezien, 1996); or that in some cases no relationship exists at all (Page & Shapiro, 1983: 189). But these studies emphasize policy outcomes rather than policy proposals. In securitization, audience reactions to policy proposals are the primary focus.

34 Variability in phrasing, time, sampling methods, and context across polls can impact framing effects by activating different schemas and cognitions. Eichenberg (2005: 153) suggests this may be a methodological advantage though and recommends that “a reliable analysis requires the study of many survey questions that employ a variety of wordings.” Still, one way to minimize these contaminating effects and overcome this concern is to analyze only a subset of polls conducted by the same

organization that frame the question similarly over time. If the subset yields trends and findings that are

policies are used to measure securitization. Because public opinion is measured during the same time period of the content analysis, preliminary assessments can be made regarding the influence of actors on audience preferences (e.g., McLaren et al., 2017;

Simon & Jerit, 2007).

A limitation with the longitudinal public opinion data used in the case studies that follow is that key individual-level variability is not always captured. For example, while attitudes toward threats and exceptional measures may be collected, attention to particular news sources and demographic information (that can potentially influence torture and immigration attitudes) may be overlooked. This forecloses linkages between frame exposure and preferences for exceptional measures. The cross-sectional analysis overcomes this limitation by using survey data that contain this individual-level

richness. Specifically, survey data is used to estimate regression models that isolate and measure the relationship between support for exceptional measures and attention to sources. This complements the longitudinal analysis, which can show support or

opposition toward exceptional measures over time, but cannot explain definitively what drives variation below the aggregate level. Read alongside results from the content analysis, the cross-sectional models can better link individual policy preferences to media exposure. While these models provide inferential richness and variability, they only provide a snapshot of public opinion at a given moment in time. To address this limitation, multiple waves of survey datasets are used.

While cross-sectional models reveal potential alignments between attitudes and media consumption, they do not necessarily prove causal direction. Instead, they demonstrate the statistical significance, magnitude and polarity of alignments that may exist. For example, a positive statistically significant relationship between support for torture and consumption of a particular news source be interpreted as either attention to the

particular news source producing support for torture or the news source calibrating its messaging to fit audience preferences. Securitization theory and the cascading

activation model identify framing effects as a top-down process, where media frames flow to the public instead of the other way around. This process is assumed in the analyses. Certainly media actors are privy to the public mood and will tailor content to

congruent with aggregate results, confidence in findings from the super set is increased. When possible, the case studies here use such subsets to validate inferences from larger data.

align with audience preferences. This feedback loop will nonetheless strengthen audience resolve on policy preferences, leading to a continuous reproduction of framing effects. Still, the limitation associated with the uncertainty of causality is discussed in greater detail in the final chapter.

To summarize, the longitudinal and cross-sectional analyses are mutually reinforcing dimensions of audience reaction to (de)securitization frames from different sources.

The former shows how audience evaluation moves over time and compares this to the evolution of political elite and the media frames. It provides preliminary evidence of audience responsiveness to content and source frames. The cross-sectional analysis focuses on individual moments along this timeline to make definitive links between individual-level variation in support for exceptional measures and attention to different sources. It shows whether individual preferences mirror the frames employed by the sources they pay attention to. While the macroanalysis shows how public opinion responds to frames over time, the microanalysis statistically links audience evaluation to the sources using those frames.

3.3.3.1 Operationalization

The operationalization of audience analysis is specific to each case study and is thus expanded upon in Chapters 5 and 7; nonetheless, some procedures apply generally. In both case studies, the longitudinal analysis uses polls to measure audience acceptance of the threat (the stage of identification); and audience acceptance of exceptional measures (stage of mobilization). The stage of identification is measured using polls that ask respondents to attribute levels of importance to various issues, while the stage of mobilization is measured using polls that ask respondents to indicate their support or opposition toward specific exceptional measures (and for the immigration case study, unexceptional policy proposals). Polls are retrieved from the Roper Center for Public Opinion Research using a keyword search, yielding question-response pairs from multiple polling firms. Table 3.2 shows the search criteria used to collect the data for each security sector. Using a combination of manual and automated techniques,³⁵ question-response pairs were vetted and retained based on their ability to meet the

35 This involved an iterative process of manually reviewing question-response pairs and using a rules-based automation approach to find other similar pairs. Similar question-answer pairs were grouped. This process was repeated until all pairs were matched to a particular group or discarded as irrelevant.

following criteria: construct validity (the question-response pair measures the desired construct); consistency (the selected question-response pairs use identical or near identical wording that minimizes the range of meaning perceived by respondents); and generality (questions-answer pairs are broad and concise, with minimal contaminating cues).³⁶ Question-response pairs that did not match the logic of the stages of

identification and mobilization were removed from consideration.

36 Despite these strict criteria, the poll data is vulnerable to some limitations. While enforcing generality at the question level limits bias, it was difficult to control for other contextual influences (preceding questions, the format of the poll/interview, self-selection concerns) that may have affected responses.

This research attempts to mitigate these concerns by using question-answer pairs that have a large respondent size as well as using a large number of question-answer pairs for the time periods under study in order to offset effects in individual polls.

Table 3.2 Search Query for Polls

Terrorism Unauthorized Immigration

Keywords terrori% OR tortur% OR interrog%

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 other research on media effects (e.g., Coleman & Banning, 2006;

Hetherington, 1996; Iyengar & Simon, 1993; Kellstedt, 2000), the microanalysis employs survey data from the American National Election Studies (ANES), conducted during the general election years 2012 and 2016. The ANES surveys are conducted every general election year on a representative sample of Americans that are eligible to vote. The surveys are administered face-to-face and on the Internet. ANES data is used to estimate regression models that isolate the impact of attention to news media and political elites on evaluations of exceptional measures by controlling for demographic and contextual factors identified in previous research as significant drivers of support for those exceptional measures. These can include, for example, political ideology and partisanship (other control variables are discussed in the case study chapters).

The dependent variables, listed in table 3.3, are uniform in all models for the military sector case study: respondents were asked to indicate their levels of support for torture as a counterterrorism policy. For the immigration case study, three different exceptional measures are explored across the two surveys.

Table 3.3 Dependent Variable Selection for Microanalysis

Terrorism Unauthorized Immigration

Dependent Variable Support for torture (2012, 2016) Support for status checks (2012)

Support for constitutional amendment (2016) Support for border fence (2016)

3.4 Conclusion

In defense of methodological pluralism, Balzacq (2011a: 38) notes that “although one method could help grasp the main features of securitization... [others] could be

mutually supportive in accounting for the nuances of the design and evolution of a security problem.” In this spirit, the components of the design proposed here – discourse analysis, content analysis and audience analysis – converge upon a holistic model of securitization. What distinguishes this pluralist framework from existing securitization research is that it is driven by two complementary components: (1) a longitudinal macroanalysis that characterizes each of and highlights relationships between media, political discourse and public opinion and (2) a cross-sectional microanalysis that tests findings from the macroanalysis in a statistically robust way.

Each analysis overcomes limitations of others, and identifies, measures and explains different aspects of the securitization process. Nor are the disparate methods

incompatible with each other. Discourse analysis, for example, provides the necessary

“clues” to bound and guide the content analysis (Neuendorf, 2004: 35), which in turns offers a validity check on discourse analysis. Both contextualize and characterize securitizing actor rhetoric. Content analysis also standardizes discourse into quantitative features that can be used in conjunction with public opinion data to estimate relationships between (de)securitizing actors and their audiences. A similar symbiosis exists between the macroanalysis and microanalysis. The results of a content analysis illustrate frame competition and source differentiation, specifically linking different sources to different frames over time. Cross-sectional models can show whether individual attitudes mirror the frames used by their preferred sources of information while controlling for myriad other variables that influence opinion on the issues analyzed. This brings the generally under-analyzed audience into the

securitization framework.

Moreover, the research design described in this section provides an opportunity to blend the methods frequently used in framing literature with discourse analysis, the preferred approach for securitization scholarship. In doing so, it introduces a novel approach to both traditions by uncovering securitization dynamics at multiple levels over time. It also improves empirically upon extant research in securitization theory by studying two security issues in different sectors under a standardized research design, offering a chance to confidently determine where sector dynamics intersect and diverge.

The following chapters operationalize this methodology across two case studies:

terrorism and immigration. To maximize theoretical and empirical gain, the terrorism case study focuses on the remedy proposal frame (specifically, the torture debate) while the immigration case study focuses on both the remedy proposal and the moral

evaluation frame (evaluation of immigrants as criminals). For each case study, a discourse analysis first maps out juxtaposing identities and the competing

(de)securitizing basic discourses that emerge (Chapters 4 and 6). Special attention is given to political texts at defining moments when the identities of (de)securitizing actors are most vulnerable (Donnelly, 2013). Next, media and political discourse are quantified (using frame signifiers identified in the discourse analysis) in a time-series representation to characterize frame competition and source differentiation between political elites and the press over a sixteen-year period (Chapters 5 and 7). The content analysis reveals whether press actors and political elites acted in concert or

independently. This is compared to public opinion data during the same time period to characterize audience preferences and make preliminary assessments about

(de)securitizing framing effects. Finally, individual-level cross-sectional models demonstrate whether preferences for exceptional measures mirror information consumption habits, while controlling for other attitude drivers.

Chapter 4: The Gloves Come Off: Political Elite