RECONSTRUCTING REALITY: HOW EMOTIONS AND BIASES
ENABLE FRAMING EFFECTS AND SHAPE ATTITUDES TOWARDS
(IN)CONGRUENT EXPOSURE
Master’s Thesis
Author: Ani Oganesian
Student ID:
11726709
Supervisor: Dhr. dr. Michael Hameleers
Date of Completion: 03/06/2019
Word Count: 7038
Erasmus Mundus Master’s in Journalism, Media and Globalization
Abstract
While the mediating role of emotions in news framing has been researched, they have not been directly seen in the context of confirmation bias. This study addresses that gap in the literature. In an experimental survey design (N = 259), this study tests how prior attitudes towards
immigration enable pro- and counter- attitudinal framing. The study also analyses eight
emotional responses within negative and positive framing and previous opinions on immigration. The results show that confirmation bias is stronger when exposed to counter-attitudinal framing. The results also demonstrate that such emotions as shame, anxiety, fear, contentment, anger, and enthusiasm are the most frequent emotions reported across all the conditions. These findings deepen the understanding of the role of various emotions on framing effects, also considering specific prior attitudes.
“Your brain predicts what the scene should look and sound and feel like, then it generates a hallucination based on these predictions. <> It’s this hallucination you exist at the centre of, every minute of every day. You’ll never experience actual reality because you have no direct access to it.”
Will Storr
Introduction
The perception of reality represents its hallucinated reconstruction in the human brain; scholars in neuroscience and psychology refer to this as to the brain’s “model” of the world that controls the understanding of what is what (Clark, 1998; Kelly, Kriznik, Kinmonth & Fletcher, 2019; Storr, 2019). Moreover, the practice by which the external world is being determined in human brain is based on predictive processing (also known as predictive coding), where predictions are generating the incoming data (Clark, 2013; Kelly et al., 2019). Thereby, biases are commonly applied by humans in various practices of everyday life.
Media habits and political behavior are no exception to this. Previous research demonstrates that confirmation biases determine perceived media messages (e.g., Knobloch-Westerwick, Mothes & Polavin, 2017). Another factor that affects perception of the information are emotions – a vast amount of studies shows mediating role of emotions in news framing effect (e.g., Lecheler, Schuk, de Vreese, 2013). Framing effects, in their turn, are seen as media
messages that are imposed by media due to various reasons, such as agenda-setting (Lecheler & de Vreese, 2019). However, audiences do not simply perceive media messages based on the information. Rather, they attempt to find evidence to their prior opinions within different media framing – this term is referred in research as confirmation bias.
A limited number of studies investigated whether there are differences in how
This paper addresses that gap in literature, as well as provides further building blocks for
understanding emotions. To investigate the above, the following research question is introduced:
RQ: What are cognitive and emotional responses within congruent and incongruent to prior
attitudes framing?
The study focuses on the issue of immigration in the US, and specifically on the alarming situation at the US-Mexico border, where thousands of families of asylum-seekers have arrived for the past months and have been held there at temporary detention camps. According to a study by Pew research Centre (2013), standpoints on borders are what divides Americans within the issue of immigration in the country.
To answer the research question, this study uses an experimental design (N = 259). First, the main concepts for this study will be described based on previous literature. This will be followed by descriptions of the method, and statistical results. At the end, the results will be discussed in the light of previous literature and practical implications.
Theoretical background
Framing effects in the context of confirmation bias and perceived message credibility
Research on news framing dates back more than 40 years ago. Today, news framing is a hot topic of debate among scholars; however, there is no consensus among researchers about a single definition of framing (Lecheler & de Vreese, 2019). For example, Gitlin (1980) defined news framing as patterns that shape the public discourse, and Modigliani (1989) showed that news framing is what provides the issue with its central idea. Two essential characteristics of news framing were defined by Entman (1993) about two decades ago: selection and salience. Selection suggests certain elements for an issue, such as judgments and decisions about it. Similarly, salience is a choice of information inclusivity within the news report. Lecheler and colleagues integrated both theoretical and empirical contributions and defined framing as
“patterns of interpretation that are used to classify information sensibly and process it efficiently” (Lecheler, et al.).
Based on previous studies, De Vreese (2005) proposed a typology of generic and issue-specific frames. Generic frames can be applied to a wide range of topics, whereas issue-issue-specific frames relate to narrow ones. For instance, some of the generic frames include conflict, morality, human interest, economic consequences, and some of the issue-specific frames focus on
healthcare, labor disputes, the Internet, and etc. (De Vreese, 2005). An essential characteristic of framing that studies show is a frame’s positive or negative valence (Chong & Druckman, 2007a; Lecheler & de Vreese, 2019). Accordingly, valence within a frame underlines positive or
negative sides of an issue described in the news story. Importantly, valence can have an impact on public opinion. For example, if valence of the framing of a policy within the story is positive, it can increase the public support for it (de Vreese & Boomgaarden, 2003). In other words, valence is what can become a cause of a framing effect. Valence in the context of framing effects is why frames, as Druckman & Bolsen (2011) write, are “analogous” to the existing values: they provide certain interpretations of events to affect, shape or change the public discourse. Thus, on the one hand, frames can be seen as practical tools of agenda-setting in media: they determine the newsworthiness of events, add up an angle to a story, and convey certain media values (de Vreese, 2005). Lecheler and Vreese (2019) also argue that framing should be seen as a part of the modern mediatization process: Strömbäk (2008) described mediatization as the “degree to which political actors are governed by media logic.”
On the other hand, considering that news framing suggests positive or negative valence that has an impact on the public, particular media framing may promote certain interpretations that are not in line with the essential principles of reporting such as accuracy, transparency of the sources, independence, expertise, rational argumentation, and diversity (Shapiro, 2010; Sparks, 2000; Jacobi, 2016). Moreover, the agenda-setting approach of framing contradicts the Full
News Standard that suggests that news stories should provide the audiences with the basic and necessary information that will help the citizens in shaping and updating their opinions (Zaller, 2003). This argument can be strengthened given the fact that framing potentially lacks factual content: scholars also point out that while frames emphasize some of the story’s components stronger than the others, they might not necessarily consist of any actual facts about events and subjects (Chong & Druckman, 2007b; Druckman & Bolsen 2011). Thereby, framing can be seen as a powerful tool for various influential groups and political actors to pursue their interests in media and impose their ideas on citizens – partisans often tend to interpret information in the way that it will show them in the favorable light (Gaines, Kuklinski, Quirk, Peyton, & Verkuilen, 2017). In fact, Levin and colleagues distinguished the persuasiveness of a communication – “goal” framing – as one of the three effects of valence; another one is “attribute” framing that aims at affecting the perception of subjects and events1 (Levin, Schneider & Garth, 1998).
While Lecheler and de Vreese’s implication of framing can be applied in the models where the media act as the “watchdog” of democracy, when journalists are expected to hold the ones in the power accountable (Strömbäk, 2005), it seems too optimistic within the current information environment in the US. Homophily, homogenization of content making, media division between “red” and “blue,” the intensification of political polarization and fragmentation of the audience – these factors speak in favor of the idea that ideological affiliation is what dictates agenda-setting in the media today. Although some scholars refer to the high level of political polarization in the US as to an “illusion”, others point out that it has increased over the past few decades: the number of people who became radical liberals or conservatives has grown, and it is even considered that the political and media landscape in the US is now more polarized than ever (Iyengar & Hahn, 2009). The increased polarization is understandable, considering that
1
the third one is the standard risky choice framing effect introduced by Tversky and Kahneman (1981)
Americans 50 years ago were exposed to the same TV channels and programs that were not advocating certain political actors, rather being more or less equally critical towards all of them. Another reason for the increased polarization is the availability of the news sources – new communication technologies do not seem democratically desirable as they add up to the fragmentation and personalization (Howard, 2006; Bennett & Iyengar, 2008; Jacobi, 2016; Reviglio, 2017). Studies show that American audiences nowadays tend to choose narrow, ‘niche’ media actors, and media in their turn adapt their business models to this and provide more
fragmented information to keep up with their content consumers (Iyengar & Hahn, 2009). While a number of empirical studies have shown the impact of framing on political attitudes and judgments of citizens (e.g., Valkenburg, Semetko, & de Vreese, 1999), this paper does not intend to argue that the political and media division in the US is the result of media framing effects. Instead, this paper suggests consideration of the definition of framing effects, first of all, within the modern context of political communication, where the rapid changes in information environment and developments of the technology have an impact on media behavior of the consumers. And secondly, within the context of the latest approaches on information processing within the framing effects.
Studies from psychology show that there are several approaches to cognitively processing information within frames; a classical tradition sees humans as rational thinkers, echoing the rational choice theory from economics, where individual’s actions are driven by rational thinking and decision-making when evaluating and analysing facts (Price, Tewksbury & Powers, 1997; Sher & McKenzie, 2008). But human rationality has always been a controversial subject in science. For instance, only throughout the second part of the last century, popular approaches on human rationality have shifted at least three times (by the end of the century psychology scholars compromised and stopped denying the existence of biases in lay cognition) (Lewicka, 1998).
A vast amount of studies shows that “the current view is that information users generally approach political messages with a confirmation bias” (Knobloch-Westerwick et al., 2017), and motivation is one of the essential factors that determine how humans perceive and treat facts and other kinds of information (Druckman & Bolsen, 2011). Festinger (1957) explained this by the theory of cognitive dissonance: confirmation bias is a result of individuals seeking congruency of the new information with their prior views and attitudes. Research shows that confirmation bias is also true to audiences within exposure to the same facts – media consumers interpret the same information in different ways, also most likely to favor their prior attitudes. For instance, the study by Gaines et al. (2017) demonstrates that during the Iraq war, Democrats and
Republicans perceived the same factual beliefs in different ways, and it was in line with prejudices tightened to their partisanship.
The study by Gaines et al. (2017) is an example of a framing effect that was neglected by some of the research; this paper thus suggests taking it into account. Framing effect within the context of confirmation bias, when identical information and description lead to different perceptions of that information (Sher & McKenzie, 2008), is a modern day and common phenomena. This is confirmed by much of the research that shows, that audiences today tend to choose their own media messages to come to the desired conclusion (Iyengar & Hahn, 2009; Wojcieszak & Garrett, 2018; Bennett & Iyengar, 2008; Druckman & Bolsen, 2011). Specifically, studies also show that exposure to two competing frames cancels both of them for the audiences most of the time, and exposure to a balanced frame still leads to biases in perceptions of the information – audiences do not prioritize factual information when framing includes facts (Chong & Druckman, 2007; Druckman & Bolsen, 2011). The experimental study by Druckman & Bolsen (2011) showed that people seek confirmation for their prior opinions “even if it’s not objectively accurate.” It seems like a vicious circle: the study by Wojcieszak & Garrett (2018) has shown that a group’s bias increases when the group is made salient. Furthermore, there is
strong evidence in experimental studies that demonstrates that more sophisticated members of the audience show stronger attitudes towards biases; similarly, citizens with less biased attitudes proceed information in more of an objective way (Taber & Lodge, 2006).
Based on these findings, it can be assumed that framing effect within confirmation bias not only affects the perception of the information, but also the perception of the article
credibility. Regarding credibility, generally, scholars suggest that it should be conceptualized as three different concepts (Appelman & Sundar, 2015), such as source credibility (e.g., Reich, 2011), message credibility (e.g., Hong, 2006), and media credibility (e.g., Golan, 2010). This paper focuses on article credibility, combining the following components for its measurement across the three concepts: newsworthiness, trustworthiness, credibility, and transparency. Considering that credibility evaluation is an important indicator of audiences’ media behaviour in the rapidly developing online environment (Metzger & Flanagin, 2013); and considering high the level of political polarization today (Iyengar & Westwood, 2015), the following can be hypothesized:
H1: People who are exposed to a congruent pro- or counter-article on immigration news are
more likely to perceive the article as credible than people who are exposed to an incongruent article on this issue.
H2: Confirmation biases in the form of perceived article credibility are stronger for citizens with
strong political beliefs within the counter-exposure conditions.
Emotional attitudes within (in)congruent framing exposure
This study also aims to analyze the emotional attitudes of media consumers within the exposure to pro and counter attitudinal news stories ((in)congruency). Emotions became a prevalent topic of debate among scholars of different research traditions for the past few
role in political judgment and behavior of media audiences (De Martino B., Kumaran D., Seymour B., Dolan J. R., 2006; Brader T., 2011; Lecheler et al., 2013). The impact of emotions on decision-making process is also confirmed in other areas of studies. For example, research from psychology and neuroscience shows that emotions are essential influencers on judgment and thought (Clore & Huntsinger, 2007; Verweij, Senior, Dominguez & Turner, 2015).
Specifically, about half of a century ago it was discovered that human thought is heuristic by its nature, thereby, emotions are as essential as rationality when it comes to processing a decision in the human brain – even by their definition emotions are the factors that determine the value of a stimulus (Tversky & Kahneman, 1974; LeDoux, 2002; Clore, 2011).
In political communication, political information can be seen as the stimulus. Thus, emotions are an important factor in how media consumers respond to the communicated data (Hasell & Weeks, 2016). News media, in their turn, have always had conveyed emotional elements: by pursuing their journalistic duty and reporting on complex and emotional topics and events such as crime, war, disasters, and etc.; moreover, the current information environment has seen a shift towards even more sensationalism, dramatism, and emotionality in reporting
(Graber, 1996; Zaller, 2003; Hasell & Weeks, 2016). Scholars agree that this has happened due to increased competition for the audience’s attention among media outlets: an emotional approach to news production recaptures the public as the latter prefers to select news with more of negative coverage (Bennett, 2003; Hasell & Weeks, 2016).
Research on the influence of emotions on the framing effect process is abundant
(Druckman and McDermott, 2008; Nabi, 2003; Lecheler et al., 2013). A number of studies use one of the two following approaches in their research: the affective intelligence or appraisal theory. The first one, the affective intelligence theory, reflects on the political behavior of citizens based on the dispositional and the surveillance affective systems – the dispositional system oversees habitual behavior, and the surveillance system takes over in a new and
unfamiliar situation; the two systems control decision-making processes (Lecheler et al., 2013). The second one, the appraisal theory, implies that the expansion of an emotional state varies, depending on subjective assessments of a subject or an event (Lecheler et al., 2013).
Although some scholars claim that there are several ways of cognitive perception of framing, Lecheler and colleagues (2013) consider emotions as a “potential alternative or complementary explanation for how news framing effects come about.” A number of studies empirically confirmed that emotions mediate decision-making process and political attitudes, which is a piece of powerful evidence that opposes the idea of standard accounts of human rationality (De Martino et al., 2006; Holm, 2012; Lecheler et al., 2013). Thereby, emotions can be seen as mediators of framing. It is generally assumed that positive frames generate positive emotional attitudes and negative frames – negative ones (Lecheler et al., 2013). However, research also shows that framing effect suggests emotional responses that mediate decision biases (De Martino et al., 2006) since emotions are also associated with such attitudes as motivations and goals, cognitive appraisals, and action tendencies (Hasell & Weeks, 2016).
Research on framing effects and emotions is abundant, however the two most studied emotions in existing literature are anger and anxiety as it is considered that both of these negative emotions impact changes in political and media attitudes and behaviors, and experimental studies demonstrate that anger and anxiety have a mediating effect on opinion-making (Holm, 2012; Lecheler et al., 2013; Hasell & Weeks, 2016; Otto, 2018). According to American Psychological Association's Encyclopedia of Psychology, anger is an emotion driven by the idea that someone or something a person feels “has deliberately done them wrong,” and it is also mentioned that anger makes it “difficult to think well” (APA, 2019). This is not in line with Nabi’s study (1993, p.303) that argued that angry citizens are not less careful when evaluating the information than others. What is clear from other research is that anger
more actively in the political discourse (Nabi, 2003; Hasell & Weeks, 2016). Anger is also closely associated with prejudices. For example, a study conducted by Weeks (2015) shows that anger stimulates stronger partisanships and congruent to prior attitudes biases.
Regarding anxiety, some scholars identify it with fear (e.g., Nabi, 2003; Hasell & Weeks, year; Lecheler, Bos & Vliegenthart, 2015) in terms of studying political and media behaviour. The role of anxiety on the issue of immigration was investigated by Gadarian & Albertson (2014) in their experimental project. The researchers designed a manipulated news website with both immigration and non-immigration articles to take a closer look at the anxious respondents, and the study showed that anxious individuals read the information that seems threatening to them, remember it and agree with it (Gadarian & Albertson, 2014). This is in line with other literature that also shows that similarly to anger, anxiety provokes deeper learning of information as well as more frequent exposure to news since anxiety is associated with danger and threat (Nabi, 2003; Hasell & Weeks, 2016). But unlike anger, fear, and anxiety promote rather inactivity than the desire to take action (Lecheler et al., 2013).
Contentment and enthusiasm are among positive discrete emotions that scholars often pay attention to. The effects of these emotions can be drawn as parallel lines to anger and fear since enthusiasm is associated with action tendencies and deep information-processing, whereas contentment supports immobility (Lecheler et al., 2013).
Shame, proudness, and resentment are among so-called inter-group emotions. Although some scholars claim that groups cannot have proper emotions, because they have no
consciousness, others argue against this (Schmid, 2014). Researchers Mackie, Devos & Smith conducted the Intergroup Emotions Theory (2000) that suggests that intergroup emotions are what people feel as a result of their group membership with which they identify. Based on empirical evidence, IET considers increased salience as a factor that increases group-based
emotions (Mackie & Smith, 2018). As Schmid (2014) writes, it is important for humans to cultivate the feeling of unity, and this can be achieved with the use of group-based emotions.
Both emotions and biases in literature are associated with determining one’s identity. For example, research on confirmation bias has shown that audiences tend to shape their information environments based on their social identities (Knobloch-Westerwick et al., 2017). In his latest book “Identity” (2018), Francis Fukuyama explains, that emotions are the main drivers for pretty much anything that is happening in the world today: from the rise of nationalism to the #MeToo movement. Fukuyama uses Hegel’s understanding of the human struggle for recognition and explains that American citizens today are divided into groups seeking restitution of one’s dignity that also carry a lot of emotional weight (Fukuyama, p. 7). In his milestone study “Imagined Communities,” Benedict Anderson (1991) also described biases that are driven by emotions, and he called them “we-feeling.” Anderson’s study showed the natural character of human desire to unite with the like-minded. In any case, people are prone to identifying themselves with others who share similar values and interests and forming groups with them (Wojcieszak & Garrett, 2018).
Researchers have studied group forming behavior for over the past 100 years (Kaakinen, Sirola, Savolainen & Oksanen, 2018). According to social identity theory, groups are being formed when people are seen as members of the ingroup, and the outgroups (Tajfel & Turner, 1979), and confirmation bias leads to a disbalance in the acquisition of political information and an ingroup homogeneity (Kaakinen et al., 2018; Bennett & Iyengar, 2008; Iyengar & Hahn, 2009). It is known from the studies that members of audiences often overestimate the meaning of their perceptions and downgrade opposing ones (Kuru, Pasek & Traugott, 2017). Negative attitudes can also be seen toward the outgroups, and hostility directed at the outgroup is even considered as appropriate (Wojcieszak & Garrett, 2018; Iyengar & Westwood, 2015).
It is also known that some frames trigger stronger emotional attitudes than others, especially if they seem more important or personal to the individual (Lecheler et al., 2013). Immigration is one of the most controversial topics in the US political agenda (Gadarian & Albertson, 2014). Emotions play an essential role in mediating news framing effects opinions about immigration (Lecheler et al., 2015). This study adds up to the existing literature by investigating emotions within prior immigration attitudes. Thereby, the following research question is introduced:
Q1: What are the emotional attitudes when exposed to pro- and counter- attitudinal reporting
about immigration?
Method General design
To investigate the hypotheses and to answer the research question, the study used an experimental design that was embedded in an online survey (N = 259). Each participant was randomly assigned to one of the following three conditions (represented by three news stories about the situation at the US-Mexico border) to test different framing/no framing effects, with no statistically significant differences between the distribution to the groups:
1. A pro-immigration article highlighting the immediate need to organize better conditions for the asylum-seekers that arrive at the US-Mexico border (N = 83);
2. An anti-immigration article focusing on financial and organizational consequences facing the US government when dealing with the higher flow of the asylum-seekers that arrive at the US-Mexico border (N= 89);
3. A control condition with no frame (a neutral article) representing all the main actors of the story in a neutral way, and only based on factual information about the asylum-seekers that arrive at the US-Mexico border (N = 87).
Participants
After the removal of incomplete surveys, the final sample of participants comprised N = 259. American citizens were selected for participation as the research subject of the paper is the immigration in the US. Respondents between the ages of 18-74 were recruited (M = 2.62, SD = 1.087) through Amazon Mechanical Turk. On average, they spent about 6 minutes on the experiment (M = 348 (in seconds), SD = 209). Participants were informed that the study was conducted for academic purposes, and at the end of the experiment they were also made aware of the manipulations in the articles as well as of the true facts and numbers. When receiving the data, participants’ privacy was secured by deleting their IP addresses.
Table 1 Demographic and Political Profile of Participants
Variable Scale SD Political ideology 1 (very liberal) = 15,8%
(41), 2 =20,8 (54), 3 = 13,5% (35), 4 (moderate) = 24,7% (64), 5 = 8,5% (22), 6 = 11, 2% (29), 7 (very conservative) = 5,4 (14) 1.765 Ethnicity White = 73, 4% (190),
Black or African American = 18,1% (47), American Indian or Alaska Native = .8% (2), Asian = 4,6% (12), Native Hawaiian or Pacific Islander = .8% (2), Other = 2,3 % (6) 1.050 Gender Female = 42,1% (109), male = 57,9% (150) .495 Age 1 (18 – 24) = 8,5% (22), 2 (25–34) = 47,9% (124), 3 (35–44) = 25,5% (66), 4 (45–54) = 12% (31), 5 (55–64) = 3,5% (9), 6 (65– 74) = 2,7% (7), 7 (75+) = 0% (0) 1.087
Stimul us materi al T he stimulu s material represented one news story per condition as it was described earlier. Following the examples from other studies, (i.e. Lecheler et al., 2013), the core information – in this case, the factual – was kept identical across all of the conditions. At the same time, identical factual information was interpreted differently in the pro-immigration and anti-immigration framings as one can imagine; the headlines and the leads of the articles reflected their respective tone, too. The neutral article (no frame that was also the control condition) avoided any interpretations or whatsoever and its tone was kept neutral. Moreover, the neutral article was based on Ethical Journalism Network guidelines for migration reporting. The guidelines involve five main recommendations, such as: 1) focusing on facts and not biases, 2) achieving good knowledge of law and using the terminology appropriately, 3) showing humanity towards the ones in need, 4) representing all sides of the story, and finally, 5) challenging hate speech (Ethical Journalism Network, 2016). Some might argue that the third and fifth points in these guidelines can be seen as some dimensions of pro-immigration framing. It is clear that there is more empathy towards asylum-seekers in the pro-immigration article than in the anti-immigration one. However, showing humanity and avoiding discrimination when reporting on various kinds of global and humanitarian crises should not be mistaken for favoring a side in framing and seen as such. Education 1 (less than high school) =
.8% (2), 2 (high school graduate) = 12% (31), 3 (some college) = 20,1% (52), 4 (2 year degree) = 7,3% (19), 5 (4 year degree) = 48,6% (126), 6 (professional degree) = 9,7 % (25), 7 (doctorate) = 1,5% (4) 1.303
News consumption 1 (never) = 1,5% (4), 2 = 23,2% (60), 3 (a few times a week) = 20,5% (53), 4 = 18,9% (49), 5 (every day) = 35,9% (93)
The articles were manipulated but based on real facts and numbers. The facts and the numbers were merged from two reports about the situation published in the New York Times. The tone and the language in the pro- and anti- immigration framings were inspired by reports from BuzzfeedNews and FoxNews respectively. In addition, the layout reminded a news story from an online-newspaper and it remained the same across all conditions. Also, the authorship, the font, and the illustration were kept constant in all the three articles.
Procedure
First of all, the online survey was posted on MTurk, offering potential participants (MTurk “workers”) to answer a questionnaire about their opinions on media and politics as well as sharing their feelings about immigration in their country. After entering the survey,
participants were asked a few general profile questions about their demographics, political ideology, media habits, and immigration attitudes. They were then randomly assigned to one of the three conditions and asked to read the news story. After the exposure to the news story, the respondents were asked questions about the perceived credibility of the message. They were also asked to share the emotions that they felt after reading the article. In the end, a manipulation check question was asked. The data was collected between the 10th and 15th of May 2019.
Measures
Firstly, an independent variable (“conditions”) depending on the level of exposure to pro-or counter versus neutral immigration news was computed to test H1, H2, as well as to answer
Q1, where 1 meant neutral (control), 2 – pro-immigration framing, and 3 – anti-immigration
framing. Then, another independent variable (“prior immigration attitudes”) was computed to test H1, H2, and to answer Q1 – prior immigration attitudes – based on a pre-exposure question about position towards immigration in the US (“To what extent do you oppose/support
immigration in the US?”). The answers were measured on a 7-point scale (1 = strongly disagree, 7 = strongly agree), and a principal component factor analysis allowed extraction of two groups
where 0 meant negative and 1 meant positive, depending on respective immigration attitudes (Cronbach’s alpha = .868). A few more respondents happened to appear in the negative attitudinal group (N = 138) than in the positive attitudinal one (N = 121).
Cognitive attitudes: article credibility
Four independent variables were computed to test H1 and H2, based on the conditions variable combined with prior immigration attitudes variable to make (in)congruence within the exposure and respondents’ attitudes visually accessible. Thus, the following congruent and incongruent variables were created:
the group supporting immigration and exposed to pro-immigration news story;
the group opposing immigration and exposed to anti-immigration news story;
the group opposing immigration and exposed to pro-immigration news story;
the group supporting immigration and exposed to anti-immigration news story. The dependent variable for H1and H2 – cognitive attitudes – was operationalized as perceived credibility of the articles. It was measured with four items on a 7-point scale (1 =
strongly disagree, 7 = strongly agree): newsworthiness, trustworthiness, credibility, and
transparency (“To what extent do you agree/disagree that the information in the article was...”). A principal component factor analysis for article credibility question extracted one component with high reliability scale (Cronbach’s alpha= .891), allowing merging the four items into one.
However, a new independent variable was computed to test H2. It was based on a pre-exposure question about political ideology, and the respondents who indicated strong political beliefs formed that variable (“What is your political ideology?” 1 = very liberal, 7 = very
conservative). Then, the perceived credibility was measured, considering strong liberals and
strong conservatives who were exposed to counter-attitudinal framing.
To indicate significant associations within H1 and H2, a multiple linear regression was tested, where the independent variables were treated as predictors.
Emotional attitudes
After the exposure, participants were asked to state their emotions after reading the article (“After reading the article, I feel…”). They could choose from eight emotions:
enthusiasm, content, fear, anger, proudness, shame, anxiety, and resentment; they also had the option to choose more than one emotion to describe their feelings about the news story. This was the core question that was used to investigate the dependent variable in Q1. To measure the outcome, the four groups that were mentioned above were recoded into one variable, where the group supporting immigration and exposed to pro-immigration news story meant 1, the group opposing immigration and exposed to anti-immigration news story meant 2, the group opposing immigration and exposed to pro-immigration news story meant 3, and finally, the group
supporting immigration and exposed to anti-immigration news story meant 4. Finally, crosstabs were made for one of each emotion separately using the new variable that showed frequencies of reported emotions within each of the group. Also, a one-way between subjects ANOVA was conducted separately for each item of emotions to compare the effects of the independent variables within the conditions.
Results Manipulation check
A manipulation check question was asked to measure participants’ comprehension of the framing/no framing within the conditions. The respondents were asked whether they agree or disagree with six statements on a 7-point scale (1 = strongly disagree, 7 = strongly agree), and the statements measured the perceived tone of the articles (positive/negative/neutral) and perceived position towards migrants that was expressed in the news stories
(supporting/opposing/balanced position).
First, a principal component factor analysis was run, and it extracted the three expected components mentioned above. Then, a one-way ANOVA test was run regarding the components,
conditions, and the manipulation check question (M = 4.22, SD = 1.803). The manipulation check resulted in significant differences (p = .000) between the conditions and it showed that the respondents comprehended the respective frames/no frame in the news stories. Before
conducting the actual experiment, an initial pre-test was run with 15 participants and it also confirmed the comprehension of the manipulation.
Confirmation bias within (in)congruent framing
This study tested cognitive information processing in terms of confirmation bias within different framings and exposure, as well as emotional attitudes of respondents when exposed to pro- and counter- attitudinal reporting about immigration. The study had two main assumptions. The first hypothesis stated that the opinion of respondents on the credibility of the exposed news story differs depending on their prior perceptions on immigration and attitudes towards it. In other words, cognitive attitudes towards pro- and counter- attitudinal framing on immigration depend on prior standpoints. The interactions between the dependent variable and prior attitudes towards immigration (see Model 1) indicated that there is a significant effect between the four groups and perceived article credibility (F =7.894, p =.000b; R2=14.92). However, it was further
examined that there was no significant effect on article evaluation within the congruent groups, thereby within the groups of respondents exposed to pro-attitudinal framings (the group
supporting immigration and exposed to pro-immigration news story – “supporting/positive exposure” p =.421; the group opposing immigration and exposed to anti-immigration news story – “opposing/negative exposure” p =.174). At the same time, statistically significant effects were found within the incongruent groups – respondents who were exposed to counter-attitudinal framings (opposing immigration and exposed to pro-immigration news story –
“opposing/positive exposure” p =.000; group supporting immigration and exposed to anti-immigration news story – “Supporting/negative exposure” p =.035). Therefore, it can be said that confirmation biases were found and hypothesis 1 was partly confirmed.
Table 2 Perceived credibility of the articles ( N = 259)
Model 1 Model 2 Model 3
Variable B SE B β B SE B β B SE B β Supporting/positive exposure .222 .275 .053 .192 .276 .045* .189 .277 .045* Opposing/negative exposure -.168 .124 -.091** -.151 .127 -.081** -.151 .127 -.081** Opposing/positive exposure -.430 .087 -.326** -.434 .087 -.329** -.453 .090 -.344** Supporting/negative exposure -.131 .062 -.141** -.119 .064 -.129** -.131 .070 -.141** Strong liberals -.059 .195 -.020** -.084 .206 -.028** Strong conservatives -.346 .249 -.089** -.397 .259 -.102** Strong liberals/counter exposure .336 .600 .038* Strong conservatives/counter exposure .770 .866 .057 R2 .111 .117 .122 F 7.894 5.586 4.323 Note: *p < 0.05; **p < 0.01.
Interactions between strong partisanship and perceived credibility
The second assumption (H2) of this research was that political affiliation is what has a big impact on perceived media exposure among citizen with strong political beliefs – strong liberal and strong conservative supporters. ANOVA test within the regression analysis showed statistically significant results (strong liberals p = .576, strong conservatives p = .375). However, this analysis did not find evidence to confirm hypothesis 2, since both of these groups were expected to perceive the articles not credible. However, as it can be seen from the Table 2, the respondent with strong liberal views showed more predictable outcome when exposed to anti-immigration article than the respondents with strong conservative standpoints when they were
exposed to the pro-immigration one (β = .038; β = .057). This is an important finding in the understanding the role of partisanship when it comes to counter-attitudinal framing.
Emotional attitudes
Finally, the research question (RQ1) investigated emotional attitudes towards pro- and counter- attitudinal media exposure. The results showed statistically significant associations between the following studied emotions and prior immigration attitudes across all the conditions: enthusiasm (p = .003), anger (p = .007), fear (p = .006), and shame (p = .001). At the same time, such emotions as contentment, anxiety, resentment, and proudness did not show statistically significant associations (p = .953; p = .440; p = .267; p = .523; respectively).
However, to answer the RQ1, and to have a closer look at the emotions that the respondents expressed when they were exposed to congruent and incongruent framing, it is important analyze the frequencies of these emotions. Graphs 1 and 2 show emotions reported within four groups of respondents: two groups that were exposed to a congruent to their prior views news story about immigration and two incongruent.
The two groups within congruent to prior attitudes exposure are the group supporting immigration and exposed to a pro-immigration article, and the group opposing immigration and exposed to anti-immigration article. As it can be seen from the Graph 1, shame stands out for the first group (26%), and three emotions at the same time – anxiety, fear and contentment – stand out for the second one (20% each). Anxiety was also common among respondents from the first group (19%). The only non-expressed emotion for the first group is enthusiasm with 0%,
whereas that number is relatively high for the second group – 13%. Both of the groups expressed resentment and at a similar level – with 7,5% for the first group and with 8% for the second one.
The two groups within in congruent to prior attitudes exposure are: the group opposing immigration and exposed to pro-immigration framing, and the group supporting immigration exposed to anti-immigration news story. The graph shows that the most frequent emotions that were reported by respondents from the first group are anger and shame (26% and 24%,
respectively). The most expressed emotion among respondents from the second group is
contentment (19%), with fear and enthusiasm following (15 and 16%, respectively). At the same time, enthusiasm is the least expressed emotion for the first group – only 1% of respondents chose that option. Proudness and shame are the least expressed emotions among respondents from the second group, with 8% each.
Graph 2 Emotions within incongruent to prior attitudes exposure (in percentages) 0 5 10 15 20 25 30
Enthusiastic Content Afraid Angry Proud Ashamed Anxious Resentment
Discussion
This thesis sought to measure cognitive and emotional attitudes towards immigration within framing effects as well as understand connections between confirmation biases, framing and emotions. By integrating eight emotions (enthusiasm, contentment, fear, anger, proudness, shame, anxiety, and resentment) into an experiment that consisted of different framing exposure and examining prior attitudes towards immigration, this paper has three main conclusions.
Firstly, it was found that confirmation biases are more pronounced when audiences are exposed to counter-attitudinal framing. This finding adds to the existing literature by expanding on the link between confirmation bias, pro- and counter- attitudinal exposure. The second main finding is that attitudes towards immigration and specifically the crisis at the US-Mexico border go beyond political ideology; instead, the attitudes towards this issue represent complex process of decision-making. And, finally, framing effects do not simply lead to positive and negative emotional responses based on prior positive or negative attitudes towards an issue – rather, they are driven by prior emotional attitudes within the in-group and out-group behavior: as shame,
0 5 10 15 20 25 30
Enthusiastic Content Afraid Angry Proud Ashamed Anxious Resentment
anxiety, fear, contentment, anger, and enthusiasm are the emotions that stand out within immigration discourse across all the groups of respondents.
Previous research has shown that confirmation bias is driven by political affiliation (Knobloch-Westerwick, Mothes & Polavin, 2017). Moreover, partisanship is considered the new form of social identity nowadays in the US (Iyengar & Westwood, 2015); as Fukuyama writes, “the left has focused less on broad economic equality and more on promoting the interests of a wide variety of groups perceived as being marginalized”, whereas “the right is redefining itself as patriots who seek to protect traditional national identity” (Fukuyama, pp. 6-7). However, studies on immigration demonstrate that this topic is sensitive to many citizens and the
perception does not simply depend on the party standpoint (Gadarian & Albertson, 2014; Seate & Mastro, 2015; Wojcieszak & Garrett, 2018), which goes in line with the results of this paper. Another finding of this paper that goes in accordance with previous research is that framing effects should not be seen through lenses of positive and negative valence (e.g., Lecheler et al.; Otto, 2018). It was already discovered by Entman (1993) that perception of frames happens within four locations, and one of them is the receiver – framing effect depends on the individual’s prior knowledge and attitudes.
Limitations and further research
One of the limitations of this paper is that the dependent variable – confirmation bias – was operationalized by perceived article credibility which might be seen as an indirect indication of a bias. Another limitation is that it is conducted on specifically American context. Given the different nature of politics across different parts of the world, generalization of the outcomes of the study is questionable. At the same time, the study was conducted while the crisis on the US-Mexico border was ongoing. Thereby, there was a high chance that subjects were exposed to the topic within their real information environments, and have explicit prior attitudes towards the issue before participating in the experiment. Finally, the experiment measures short-term effect
of framing on emotions. Further studies should apply a longitudinal design for an in-depth examination.
In terms of further research, studies should examine in-depth the reasons behind certain emotional attitudes by perhaps generating a different study method that will allow conducting interviews with participants. Further research can also measure emotions towards immigration before the manipulation to examine the differences and similarities (if any) after the exposure.
Practical applications
Prior attitudes formed before the consumption of information can lead to decrease in critical thinking and informed decision-making (Reviglio, 2017). Confirmation biases, ingroup and outgroup biases increase the distance between individuals with different points of view and add up to political polarization. The question of more fueled fragmentation “has been debated by social and political theorists ever since the Internet entered academia” (Reviglio, 2017). Scholars underline the fact that acquaintance with different ideas and communities is essential to
functioning of democracy (Knobloch-Westerwick, et al., 2017).
Mandel (2014) pointed out that framing effects have long been seen as a supporting evidence to the idea that the process of decision making in human brain is irrational. However, cognitive decision-making is as natural to human beings as emotional attitudes, and being aware of this fact can further help individuals manage their attitudes and develop a less hallucinated perception of the reality.
References:
1. American Phycological Assosiation. (2010). Anger. URL: https://www.apa.org/topics/anger/
2. Anderson, B. (1991). Imagined Communities. Retrieved from
https://is.muni.cz/el/1423/jaro2016/SOC757/um/61816961/Benedict_Anderson_Imagine d_Communities.pdf
3. Appelman, A. S. & Sundar, S. S. (2015). Measuring Message Credibility: Construction and Validation of an Exclusive Scale. Journalism and Mass Communication Quarterly, 93(1), 59–79.
4. Bennett, W. L. (2003). The burglar alarm that just keeps ringing: A response to Zaller.
Political Communication, 20(2), 131–138.
5. Bennett,W. L., & Iyengar, S. (2008). A new era of minimal effects? The changing foundations of political communication. Journal of Communication, 58, 707– 731.doi:10.1111/j.1460- 2466.2008.00410.x
6. Brader, T. (2011). The Political Relevance of Emotions: “Reassessing” Revisited. Political
Psychology, 32(2), 337-346.
7. Chong, D., & Druckman, J. N. (2007a). Framing theory. Annual Review of Political
Science, 10, 103–126.
8. Chong, D., & Druckman, J. N. (2007b). A theory of framing and opinion formation in competitive elite environments. Journal of Communication, 57, 99–118.
9. Clark, A. (1998). Being there: Putting brain, body, and world together again. Cambridge, MA: MIT Press.
10. Clore & Huntsinger, 2007 How Emotions regulate Judgment and Thought. Trends in Cognitive Sciences, 11(9), 393–399.
11. De Martino B., Kumaran D., Seymour B., Dolan J. R. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, 313, 684–687.
12. de Vreese, C. H. & Boomgaarden, H. 2003. Valenced news frames and public support for the EU: Linking content analysis and experimental data. Communications: The European
Journal of Communication, 3, 261–281.
13. de Vreese, C. H., (2005). News framing: Theory and typology. Information Design Journal
+ Document Design, 13(1), 51-62.
14. Druckman, J. N., & Bolsen, T. (2011). Framing, motivated reasoning, and opinions about emergent technologies. Journal of Communication, 61(4), 659-688. doi: 0.1111/j.1460-2466.2011.01562.x
15. Entman, R. B. (1993). Framing: Toward clarification of a fractured paradigm. Journal of
Communication, 43, 51–58.
16. Fassin, D. (2013). On Resentment and Ressentiment: The Politics and Ethics of Moral Emotions. Current Anthropology, 54(3), 249 - 267.
17. Festinger, L. (1957). A theory of cognitive dissonance. Palo Alto, CA: Stanford University Press.
18. Fukuyama, F. (2018). Identity. London, England: Profile Books Ltd.
19. Gadarian, S. K. & Albertson, B. (2014). Anxiety, Immigration, and the Search for Information. Political Psychology, 35(2), 33-164. doi: 10.1 11 l/pops. 12034
20. Gaines, B. J., Kuklinski, J. H., Quirk, P. J., Peyton, B. & Verkuilen, J. (2017). Same facts, different interpretations: Partisan motivation and opinion on Iraq. The Journal of
Politics, 69(4), 957–974. doi: 10.1111/j.1468-2508.2007.00601.x
21. Gitlin, T. (1980). The whole world is watching. Berkley: University of California Press. 22. Golan, G. (2010). New Perspectives on Media Credibility Research. American Behavioral
23. Graber, D. A. (1996). Say it with pictures. The Annals of the American Academy of
Political and Social Science, 546, 85–96. Retrieved from
http://www.jstor.org/stable/1048172.
24. Hasell, A. & Weeks, B. E. (2016). Partisan Provocation: The Role of Partisan News Use and Emotional Responses in Political Information Sharing in Social Media. Human
Communication Research, 42, 641–661. doi:10.1111/hcre.12092
25. Holm, E. M. (2012). Emotions as mediators of framing effects. Arhaus: Politica 26. Hong T (2006) The influence of structural and message factors on website credibility.
Journal of the American Society for Information Science and Technology 57(1): 114–
127.
27. Howard, P. N. (2006). New media campaigns and the managed citizen. New York: Cambridge University Press.
28. Iyengar, S., & Hahn, K. S. (2009). Red media, blue media: Evidence of ideological selectivity in media use. Journal of Communication, 59(1), 19–39. doi: 10.1111/j.1460-2466.2008.01402.x
29. Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707. doi:
10.1111/ajps.12152
30. Jacobi, C. A. (2006). The quality of political news in a changing media environment. 31. Kelly, M. P., Kriznik N. M., Kinmonth A. L. & Fletcher P. C. (2019). The brain, self and society: a social-neuroscience model of predictive processing. Social Neuroscience, 14(3).
32. Knobloch-Westerwick, S., Mothes, C., & Polavin, N. (2017). Confirmation bias, ingroup bias, and negativity bias in selective exposure to political information. Communication
Research.
33. Kuru, Pasek & Traugott, (2017). Motivated Reasoning in the Perceived Credibility of Public Opinion Polls Forthcoming in Public Opinion Quarterly. doi: 10.1093/poq/nfx018
34. Kaakinen, M., Sirola, A., Savolainen, I., & Oksanen, A. (2018). Shared identity and shared information in social media: Development and validation of the identity bubble
reinforcement scale. Media Psychology.Advance. Retrieved from http://dx.doi.org/10.1080/15213269.2018.1544910
35. Lecheler, S. & de Vreese, C.H. (2019). News Framing Effects: Theory and Practice. [Kindle version]. Retrived from www.amazon.com
36. Lecheler, S., Bos, L., Vliegenthart, R. (2015). Journalism & Mass Communication
Quarterly, 92 (4), 812–838. DOI: 10.1177/1077699015596338
37. Lecheler, Schuk, de Vreese, (2013). Dealing with feelings: Positive and Negative Discrete Emotions as Mediators of News Framing Effects. Communications, 38(2), 189–209. 38. LeDoux, J. E. (2002). Synaptic Self: How Our Brains Become Who We Are. London,
England: Penguin.
39. Levin I. P., Schneider S. L. & Garth, G. J. (1998). All Frames Are Not Created Equal.
Organizational Behavior and Human Decision Processes, 76(2), 149–188.
40. Lewicka M. (1998). Confirmation Bias. Retrieved from: https://doi.org/10.1007/978-1-4757-2901-6_9.
41. Mackie D. M. & Smith E. R. (2018). Intergroup Emotions Theory: Production, Regulation, and Modification of Group-Based Emotions. Advances in Experimental Social
Psychology, 58. https://doi.org/10.1016/bs.aesp.2018.03.001
42. Mandel, D. R. (2014). Do Framing Effects Reveal Irrational Choice? Journal of
Experimental Psychology: General, 143(3), 185 – 1198. DOI: 10.1037/a0034207
43. Mandel, David R. (2014). Do Framing Effects Reveal Irrational Choice? Journal of
Experimental Psychology: General, 143(3), 1185-1198.
44. Metzger, M. J. & Flanagin, A. J. (2013). Credibility and Trust in Online Environments: the Use of Cognitive Heuristics. doi:10.1016/j.pragma.2013.07.012
45. Nabi, R. L. 2003. Exploring the framing effects of emotion. Communication Research, 30, 224–247.
46. Pew Research Center, (2013). How Americans See the Illegal Immigration, the Border Wall and Political Compromise. URL:
https://www.pewresearch.org/fact- tank/2019/01/16/how-americans-see-illegal-immigration-the-border-wall-and-political-compromise/
47. Price V., Tewksbury D., Powers E., 1997. Switching trains of thought. Communication
Research, 24, 481-506.
48. Price V., Tewksbury D., Powers E., 1997. Switching trains of thought. Communication
Research, 24, 481-506.
49. Reich, Z. (2011) Source credibility and journalism: Between visceral and discretional judgment. Journalism Practice 5(1): 51–67.
50. Reviglio, U. (2017). Serendipity by Design? How to Turn from Diversity Exposure to Diversity Experience to Face Filter Bubbles in Social Media. Retrieved from Google Scholar.
51. Shapiro, I. (2010) Evaluating Journalism: Towards an assessment framework for the practice of journalism. Journalism Practice, 4(2), 143-162.
52. Sher, S. & McKenzie, C. R. (2008). Framing Effects and Rationality. Oxford Univerity Press.
53. Sparks, C. (2000). In Tabloid Tales: Global Debates over Media Standards. Oxford and Lanham: Rowman & Littlefield Publishers.
54. Stets J. E. (2005). Examining Emotions in Identity Theory. Social Psychology Quarterly, 68(1). Retrieved from https://www.jstor.org/stable/4148780
55. Storr, W. (2019). The Science of Story Telling. London, England: Harper Collins Publishers.
56. Strömbäck J. (2005). In Search of a Standard: Four models of democracy and their normative implications for journalism. Journalism Studies, 6(3), 331–45.
57. Strömbäck J. (2008). Four phases of mediatization: An Analysis of the Mediatization of Politics. The International Journal of Press/Politics, 13(3), 228–246.
58. Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50(3), 755–769. doi:10.1111/j.1540- 5907.2006.00214.x
59. Tversky, A., Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Retrieved from Google Scholar.
60. Valkenburg, P. M., Semetko, H. A. & de Vreese, C. H. (1999). The Effects of News Frames on Readers’ Thoughts and Recall. Communication Research, 26(5), 550–569.
61. Verweij, M., Senior T. J., Dominguez, D. & Turner, R. (2015 )Emotion, Rationality, and Decision-Making: How to Link Affective and Social Science with Social Theory. doi: 10.3389/fnins.2015.00332
62. Vreese, C. H. de, & Boomgaarden, H. (2003). Valenced news frames and public support for the EU: Linking content analysis and experimental data. European Journal of
Communication, 3(4), 261–281.
63. Weeks, B. E. (2015). Emotions, Partisanship, and Misperceptions: How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political
Misinformation. Journal of Communication, 65, 699–719. doi:10.1111/jcom.12164 64. Wojcieszak, M. & Garrett, R. K. (2018). Social Identity, Selective Exposure, and
Affective Polarization: How Priming National Identity Shapes Attitudes Toward Immigrants Via News Selection. Human Communication Research, 44(3), 247–273. 65. Zaller, J. (2003). A new standard of news quality: Burglar alarms for the monitorial
