International Journal of Public Opinion Research Advance Access published November 10, 2015
International Journal of Public Opinion Research
ß The Author 2015. Published by Oxford University Press on behalf of The World Association
for Public Opinion Research. All rights reserved.
doi:10.1093/ijpor/edv040
Sebastián Valenzuela and Ingrid Bachmann
Pontificia Universidad Católica de Chile, Santiago, Chile
Abstract
Most work deals with the effects, not antecedents, of people’s exposure to disagreement within their social networks. Here, we elaborate on the role played by a major
psychological driver of public opinion: emotions. Drawing from cognitive and appraisal theories, we explore the association between pride, anger, and disagreeable
political talk. Three studies—based on cross-sectional and panel surveys conducted
in electoral and nonelectoral settings in Chile, the United States, and Switzerland—
confirm that there is a significant relationship between feelings of pride toward political objects and discussing with people with ideas different from one’s own. Anger,
in contrast, is not a significant predictor of cross-cutting talk. We elaborate on these
findings and propose directions for future research.
Emotions play an important role in the formation, expression, and mobilization of public opinion. Most of the available work, however, focuses on
mediated, rather than interpersonal, communication. The current study
seeks to improve our understanding of the role played by emotions in
public opinion by focusing on political discussion, which has been defined
as ‘‘episodes of political conversation [. . .] that take place between the nonelite members of a political community’’ (Schmitt-Beck, 2008, p. 341). But
instead of studying frequency of discussion, we focus on its content, namely, the
level of political disagreement among discussion partners—a hotly contested
topic (cf., Huckfeldt, Johnson, & Sprague, 2004; Mutz, 2006; Nir, 2011;
Valenzuela, Kim, & Gil de Zúñiga, 2012). More specifically, we make the
case for including emotions in the list of determinants of cross-cutting talk in
similar fashion to work linking affective variables to media use and
All correspondence concerning this article should be addressed to Sebastián Valenzuela, Pontificia
Universidad Católica de Chile, Alameda 340, of. 703, Santiago, Chile. E-mail: [email protected]
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Pride, Anger, and Cross-cutting Talk: A
Three-Country Study of Emotions and Disagreement
in Informal Political Discussions
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The Concept of Disagreement and Its Antecedents
One of the tenets of deliberative democracy is that people need to hear and
talk to the other side to be aware of other individuals’ rationales and
arguments (Habermas, 1989). Exposure to political disagreement can be an
eye-opening experience and may result in informative—rather than reinforcing—cross-cutting interactions. Such heterogeneity allows for a qualitatively
different type of communication: it is not political talk for the sake of it, but
an exchange of ideas, rationales, and arguments (Mutz, 2006).
Prior work highlights several outcomes of discussion with non-like-minded
people, including a more thorough analysis of issues and information, a better
assessment of people’s perception of the distribution of public opinion, and a
higher level of political engagement (Huckfeldt et al., 2004, but see Mutz,
2006). Past research, for instance, shows that disagreement can be an important factor in motivating discussants to process political information more carefully (Eveland, 2004).
Although disagreement is one of the most-studied aspects of interpersonal
political discussion, different researchers define the concept in different terms.
Mutz (2006) conceives disagreement in terms of exposure to political views in
direct opposition to one’s own. Huckfeldt and his colleagues (2004), in contrast, define it as lack of agreement. Thus, as Nir (2005, 2011) has noted,
disagreement in social networks can be conceptualized as either competition or
opposition of political perspectives. A competition approach refers to a
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participation (Brader, 2006; Kühne, Schemer, Matthes, & Wirth, 2011;
Marcus, Neuman & MacKuen, 2000). We do this by testing the proposition
that certain emotions people experience in relation to political stimuli can
increase the likelihood that they will engage in interactions with people who
hold with dissonant political opinions—a highly valued behavior by normative
democratic theorists.
We begin by reviewing extant work on political disagreement, paying
particular attention to its determinants. We then briefly review the role
played by emotions on political behavior. Then, we introduce cognitive and
appraisal theories (Lerner & Keltner, 2000; Nabi, 1999) and make the case for
extending it to disagreeable discussion. Following, we posit our hypothesis and
research question, which we then test using data from cross-sectional and
panel surveys conducted in Chile, the United States, and Switzerland in electoral and nonelectoral contexts. Our rationale for a comparative design is that if
we find similar results across cultural settings and operationalization of key
variables, we will be more confident about the generalizability of our theoretical propositions. The final section discusses the implications of the results,
as well as limitations and directions for future research.
PRIDE, ANGER AND CROSS-CUTTING TALK
3
Emotions in Research on Public Opinion
Traditionally, emotions have not been deemed as central to citizenship and
public opinion, but now it is common to speak of an ‘‘affect effect,’’ at least
regarding the way emotions interact with political decision-making (Neuman,
Marcus, Crigler, & MacKuen, 2007). Emotions are, after all, constructed and
understood in response to social situations: They have a social value and
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mixture of discussants that agree and disagree in their political views. An
opposition approach, instead, refers to the amount of sheer disagreement;
how hostile is an individuals’ network of discussants to that person’s political
opinions. In this research, we focus on discussion disagreement as amount of
opposition encountered by an individual within his/her network of political
discussants. We do so because it is this aspect that is most critical for the idea
of deliberation and deliberative democracy (Gastil, 2008).
Interestingly, most existing research focuses on the consequences of crosscutting interactions—beneficial and otherwise—rather than its antecedents.
The relatively scant literature on the determinants of discussion disagreement
yields four major factors: media use, socialization processes, network size, and
resources (material and/or psychological). Indeed, media consumption is associated to valuing exposure to cross-cutting interactions (Gil de Zúñiga,
Bachmann, Hsu, & Brundidge, 2013). Similarly, agents of socialization, such
as the family and school life, have been linked to valuing both hearing and
talking to ‘‘the other side.’’ So-called concept-oriented families (Chaffee,
McLeod, & Wackman, 1973), for instance, encourage children to express
their convictions and consider different sides of an argument, which in
turns favors heterogeneous discussion (Borah, Edgerly, Vraga, & Shah,
2013). In addition, large discussion networks—even if they are apolitical—
increase the chances for encountering political difference (Buttice, Huckfeldt,
& Ryan, 2009). At the level of resources, Mutz (2006) found that disagreeable
political discussion is predicted by the typical indicators of apolitical citizens,
such as those with less education, less political knowledge, lower interest in
elections, and weaker partisan identification. Likewise, some personality traits
may act as resources, predisposing individuals to specific communication behaviors (Bachmann, Correa & Gil de Zúñiga, 2012).
Taken together, the known antecedents of disagreeable political talk refer
to habits (e.g., media use), stable individual differences (e.g., socialization,
traits or partisanship) or contextual variables (e.g., network size). What is
missing is an account of the short-term factors that may influence engagement
in disagreeable political talk. Below, we argue one such temporary influence
lies in emotions—a factor that prior research has found is predictive of political behavior, including communication practices.
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Seeking Disagreement as an Affective Goal
Affect is pervasive in the way people see and interpret the world. Indeed,
scholars and lay people alike know that people behave differently in different
moods, and there is growing empirical evidence that specific emotions carry
over from past situations to color future choices—including exposure to crosscutting talk (e.g., Han, Lerner, & Keltner, 2007; Lerner & Keltner, 2000;
Lyons & Sokhey, 2014; Nabi, 2002; Yez, 2013).
Forgas’ (1995) Affect Infusion Model proposes that affective states contain
informative cues regarding the justifiability, fairness, and effortfulness of
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impact and are evaluated within social contexts, such as discussion networks
(Shields, 2002).
Past research has shown that emotions play a role—among other processes—in information seeking (Brader, 2006), political opinion formation
(Kühne et al., 2011), and political participation (Valentino, Brader,
Groenendyk, Gregorowicz, & Hutchings, 2011). Although Neuman and colleagues (2007) count 23 theories and models linking emotions and cognition,
the dominant paradigm relating emotions to public opinion is, thus far, affective intelligence theory, or AIT (Marcus et al., 2000). AIT posits that
people have a dual emotional system that produces specific emotional appraisals, which in turn determine thought (e.g., information-processing and
cognitive activities) and behavior (e.g., media use, discussion, or political participation). While the disposition system triggers emotions that fall along the
continuous ranges of happiness or satisfaction, the surveillance systems gives
rise to emotions of anxiety and unease (Brader, 2006).
Although prior research has used AIT to study the role played by emotions on disagreeable talk, the results of these analyses run contrary to the
expectations derived from the theory. Lyons & Sokhey (2014), for example,
found that anxiety toward candidates during the 2008 U.S. Presidential
Election was not conducive to cross-cutting talk, despite the fact that AIT
puts anxiety at the center of information seeking. This may well be because
AIT applies mostly to campaign information obtained through media, not to
processes of interpersonal communication. Another possibility is that the inconsistent results reflect some inherent weakness in the theory’s predictive
power. Indeed, some authors criticize relying on dimensional models to emotions for being too simplistic, favoring instead a discrete view of emotions
(e.g., Nabi, 2010). Despite the theoretical and empirical merits of AIT, time is
ripe for exploring whether other frameworks of emotion and communication
can better address disagreeable political talk. One of these alternatives, we shall
argue, is the affect-as-information approach (Forgas, 1995), which we discuss
next.
PRIDE, ANGER AND CROSS-CUTTING TALK
5
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individuals’ actions, and thus directly influence cognitive processing to the
extent of shaping judgments and evaluative reactions to a target. Along
these lines, Nabi’s (1999) Cognitive-Functional Model and Lerner and
Keltner’s (2000) Appraisal-Tendency Framework address the influence of specific emotions on decision-making, social judgment, attitude change, and goal
setting. Both approaches opt for a discrete emotion perspective instead of a
valence-based (positive or negative) distinction, arguing that each emotion
reflects a unique person–environment relationship, is associated to different
goals, and has a particular outcome. Indeed, empirical evidence shows that
emotions of the same valence, such as fear and anger, can have different
effects on choice, whereas emotions with different valence (e.g., anger and
happiness) can have similar effects (Lerner & Keltner, 2000; Nabi, 2002;
Valentino et al., 2011). Discrete-emotion approaches argue that an emotionally
evocative stimulus prompts responses—including physiology, behavior, experience, and communication—aimed at either approach or avoidance (Lerner &
Keltner, 2000; Lerner & Tiedens, 2006) and motivates people to take emotionally consistent action that will facilitate or impede subsequent information
processing and pursuit of goals (Nabi, 1999, 2002). As a direct consequence,
we would argue that individuals can choose to modify their emotional states
and, for instance, diminish or terminate negative moods and to extend and
enhance good ones. Arguably, this includes exposure to disagreement in interpersonal communication, as cross-cutting talk itself could be a means to adjust
one’s feelings—via engagement or withdrawal—regarding political figures and
issues. In other words—and in accordance with the appraisal and cognitive
models discussed before—seeking disagreement can be an affective goal, as
emotions can provide information to decide on judgments, cognitions, attitudes, behaviors, and overall decisions.
Most theorizing on discrete emotions focuses on the effects of negative,
unpleasant emotions, such as anger and anxiety (e.g., Bushman, 2002;
Valentino et al., 2011), and tends to ignore positive ones, such as hope and
joy. Pride is one positive discrete emotion that has received some attention. It
is a self-conscious emotion experienced as a consequence of taking credit for
an achievement—either one’s own or that of someone with whom one identifies, resulting in an increase of self-worth (Lazarus, 1991; Tracy & Robins,
2007). Current research shows that pride expression is cross-culturally recognized and spontaneously displayed, and that the rewards of pride are experienced as pleasurable pride feelings, which motivate future pride-eliciting
behavior (Tracy & Robins, 2007). An ego focused yet social emotion, it can
promote expressive behaviors, such as the public announcement of an achievement (Nabi, 2002;). Past research suggests than people feeling pride see themselves as less like others (Han et al., 2007). Relatedly, Albarracı́n and Mitchell
(2004) found that people are more open to consider counter-attitudinal
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INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH
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information when they are confident they can defend their viewpoints. While
defensive confidence is not the same as pride, we suggest a similar process, as
arguably those who feel pride at a political target are motivated to boast about
that to their discussion partners and may even positively compare themselves
with non-like-minded individuals. Like in other social comparison contexts,
such downward comparison could lead to upbeat self-evaluation, reinforcing
proud people’s emotional state.
Because cross-cutting talk exposes discussants to political isolation (if their
views are on the minority), it is also a socially risky action (Mutz, 2006). After
all, it is not far fetched to conceive that disagreeable political talk may derive
in heated arguments and uncivil exchanges. Proud individuals should be more
likely to perceive these barriers as less costly and, conversely, more likely to
engage in, rather than avoid, political conflict. Moreover, pride can be
enhanced via exposure to novel and exciting communication stimuli—typical
characteristics of cross-cutting talk—for the excitement caused by incongruent
stimuli once understood (i.e., comprehension) results in positive affect
(Vorderer & Hartmann, 2009). Being in a positive affective state also lets
people be more at ease when making moral judgments about others. This is
in line with affect-as-information approaches, which postulate that affective
goals deal with the trade-off between the risks and the rewards of the decisions
made by individuals. Thus, given the findings in the research literature and
informed by our theoretical framework, our hypothesis (H1) is that a positive,
significant relationship exists between experiencing pride at a political object
and cross-cutting discussion.
Anger, on the other hand, is a negative emotion than rises from appraisals
of control and certainty (Lerner & Keltner, 2000, see also Lerner & Tiedens,
2006). It is generally elicited from the perception of demeaning offenses or
goal blockage against oneself or one’s loved ones, an emotion linked to goaloriented action tendencies (Lazarus, 1991; Nabi, 2002). In political contexts,
for instance, there is evidence that anger can be a powerful motivator of
participation (e.g., Valentino et al., 2011), including political talk (Kim,
2013). Anger lingers after the triggering event and its influence on people’s
perceptions is so strong that it often pervades unrelated judgments and decisions—guiding one’s behaviors irrespective of these having anything to do
with the source of said emotion (Lerner & Tiedens, 2006).
It is possible, then, that to deal with their anger at different political
targets, people opt to deal with the threat of further triggering by avoiding
disagreeable discussion. Relatedly, finding opposition in one’s communication
network may exacerbate, rather than offset, a state of anger (Bushman, 2002).
Prior work also shows that a major goal of interpersonal communication is to
manage ‘‘face,’’ that is, ‘‘a claimed sense of favorable social self-worth that a
person wants others to have of her or him’’ (Ting-Toomey & Kurogi, 1998,
PRIDE, ANGER AND CROSS-CUTTING TALK
7
Overview of Studies
The studies presented here are based on analyses of cross-sectional and panel
surveys that test the relationship between two sets of emotions—pride and
anger—and disagreement in discussion networks. In Study 1, a face-to-face
survey conducted with a representative cross-section of urban residents in
Chile, participants answered questions about their emotions toward a political
figure and a major environmental controversy, as well as the frequency of
discussing news or politics with people who have different ideas. In Study
2, an analysis of the American National Election Study (ANES) 2008–2009
Panel, respondents were queried how often Democratic and Republican presidential candidates made them feel, and reported the composition of their
political discussion networks, including the perceived level of disagreement
with each discussion partner. In Study 3, a two-wave telephone survey conducted with a nationally representative sample in Switzerland during a referendum campaign, we associated measures of emotions toward immigrants and
pre–post frequency of discussion disagreement regarding the naturalization of
immigrants.
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p. 187, cited by Eveland, Morey, & Hutchens, 2011). If anger increases the
likelihood of aggressive behavior and impolite exchanges of political opinions,
angry individuals may avoid disagreement to save face and manage their impressions on others, and might prefer to ‘‘vent off’’ with like-minded people—
that is, reinforce their emotion with those who share their opinions and evaluations rather than risking fueling their anger with discussion partners who
hold other viewpoints. While anger signals that something needs to be done,
exposing oneself to cross-cutting talk may not be the answer.
However, it is not clear that anger necessarily reduces cross-cutting talk.
In fact, the opposite argument could be made. As argued, anger signals to the
individual that she or he is in control. To the degree that this suggests a
secure situation (Lazarus, 1991; Lerner & Keltner, 2000), engagement in
cross-cutting talk may be more likely. Anger also prepares individuals to
participate in a conflict (Lazarus, 1991; Nabi, 2002), which may explain the
known relationship between anger and participation in conflictive political
behaviors such as street riots. Moreover, research shows that anger increases
individuals’ intention to confront other people and argue with them (Mackie,
Devos, & Smith, 2000). Considering that there are arguments for expecting
both positive and negative effects of anger on cross-cutting talk, and that both
can be explained in the context of the Affect Infusion and the CognitiveFunctional Models, we pose the following research question (RQ1): What is
the association between anger and cross-cutting talk?
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Study 1
Method
Variables
The dependent variable, discussion disagreement, was measured on a 5-point
ordinal scale (ranging from 1 ¼ never to 5 ¼ frequently; M ¼ 3.28, SD ¼ 1.52,
skewness ¼ .28) with the question: ‘‘Thinking about the people with whom
you comment the news or talk about politics, how often you talk with people
who have very different ideas from your own?’’ Subsequently, respondents
were asked about their emotional response toward then-president Sebastián
Piñera and the HidroAysén project, a planned power plant in Chilean
Patagonia that at the time of the survey had sparked massive protests from
environmentalists. Specifically, participants were asked on a 5-point scale
(ranging from 1 ¼ never to 5 ¼ frequently) how often they have felt ‘‘proud’’
and ‘‘angry’’ toward Piñera and HidroAysén, respectively (Piñera pride:
M ¼ 1.68, SD ¼ 1.19; Piñera anger: M ¼ 3.10, SD ¼ 1.62; HidroAysén pride:
M ¼ 1.73, SD ¼ 1.21; HidroAysén anger: M ¼ 2.92, SD ¼ 1.66).
In addition to these variables of interest, the following statistical controls
were included in the regression models (for the use of these controls, see
Lyons & Sokhey, 2014; Nir, 2005, 2011). Demographics included age in
years (M ¼ 42.93, SD ¼ 17.00), gender (52.06% female), and education
(M ¼ 3.80, SD ¼ 1.54). Political interest was an additive scale of two items
gauging the level of interest in political news and interest in talking with
family and friends about political affairs and politicians (Cronbach’s ¼ .76,
M ¼ 3.13, SD ¼ 1.42). Ideology was measured by asking respondents to place
themselves on a 10-point left-right ideology scale (M ¼ 4.60, SD ¼ 2.05). To
measure exposure to political news, respondents were asked in open-ended
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The study relied on a representative survey conducted in Chile’s three largest
urban regions, concentrating 62.5% of the country’s adult population. The
survey was sponsored by the School of Journalism at Universidad Diego
Portales and fielded by Feedback, a professional polling firm, between
August 19 and September 6, 2011 (for additional details, see Scherman,
Arriagada & Valenzuela, 2015; Valenzuela, Arriagada & Scherman, 2014).
The sample was a multistage area probability sample stratified by geographical
region. Because the survey is part of a larger research project that studies
youth participation in Chile, to the initial 1,000 completed interviews, an
oversample of 737 adults aged 18–29 was included in the survey design, for
a total sample size of 1,737 respondents. To reduce biased estimates, before
analysis, the data were weighted to match national parameters for age, as well
as for gender and geographic region using population estimates. The response
rate was 80%.
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PRIDE, ANGER AND CROSS-CUTTING TALK
Table 1
Predicting Disagreement in Social Networks (Study 1, Chile, Cross-Sectional Data)
DV: Frequency of discussing news or politics with people
who have different ideas from your own
Predictors
Pride
Anger
Age
Education
Gender (female)
Political interest
Political ideology
Television news
exposure
Network size
Nagelkerke R2
N (weighted)
Model 2
Emotional target:
President
B
(SE)
B
(SE)
.090**
.016
.002
.137***
.060
.200***
.029
.021
(.033)
(.023)
(.002)
(.027)
(.073)
(.029)
(.019)
(.027)
.149***
.012
.004
.154***
.042
.218***
.039*
.032
(.032)
(.023)
(.002)
(.025)
(.068)
(.027)
(.018)
(.025)
(.031)
.292***
.257***
.211
1,197
(.027)
.281
1,363
Note. Cell entries report coefficients (B) and standard errors (SE) from ordinal regression analyses with a
complementary log–log function (as recommended when higher categories are more probable than lower
categories). DV = Dependent variable.
*p < .05, **p < .01, ***p < .001 (two-tailed).
fashion how many hours on a typical week they use television (both network
and cable) for news (M ¼ 3.29, SD ¼ 1.35). Network size was operationalized
as an index by counting the types of people participants reported having
discussions about news and politics (i.e., family members, friends, co-workers
or classmates, and neighbors) (M ¼ 2.69, SD ¼ 1.42).
Results
A multiple ordinal regression analysis with a complementary log–log function
(as recommended when higher categories are more probable than lower categories was conducted, with frequency of discussion disagreement as the outcome variable. Two models were estimated, one with emotional reactions
toward the HidroAysén project, another with emotion variables on President
Piñera. In both models, all control variables were entered simultaneously with
the variables of interest.
As shown in model 1 of Table 1, feeling pride toward the HidroAysén
project is positively related to cross-cutting talk (B ¼ .090, SE ¼ .033, p < .01),
whereas feeling anger (B ¼ .016, SE ¼ .023, n.s.) is not. Repeating the same
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Model 1
Emotional target:
HidroAysén
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analysis with emotions about President Piñera confirms the strong relationship
between pride and disagreement (B ¼ .149, SE ¼ .032, p < .001), whereas
anger is not significantly correlated with disagreement (B ¼ .012, SE ¼ .023,
n.s.). These results thus support H1.
Discussion
Study 2
Method
The data for this study came from the ANES 2008–2009 Panel Study, which
interviewed a representative sample of adult U.S. citizens several times before
and after the 2008 presidential election. The present study will use data collected during January, February, and September 2008 (Waves 1, 2, and 9,
respectively), comprising the survey’s first cohort of respondents. The sample
size across these waves varied from 1,457 to 1,623, with an average response
rate of 27%. Following ANES recommendations (DeBell, Krosnick & Lupia,
2010), the analysis was conducted with the cumulative ANES panel weight,
which resulted in a weighted sample of 1,146 respondents.
Variables
In Wave 9, the ANES 2008–2009 Panel included a large battery of items on
respondents’ social networks, from which the following measure of disagreement was obtained. First, respondents were asked to name people that they
talked to about government or politics in the past 6 months (up to eight
names). Subsequently, participants were asked how different each discussant’s
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Using a representative cross-section of respondents and two sets of emotional
targets gauged in a nonelection setting, the study found that there is a positive
association between feelings of pride and having more frequent political discussions with people who oppose one’s views. At the same time, there was no
evidence that anger is predictive of disagreement. The fact that several of the
control variables behaved as expected, although they were not always significant, lends further support that the regression model was correctly specified.
For instance, as could be expected, people with more political interest and
larger social networks were more likely to encounter cross-cutting exposure in
their political discussion. These results, while consistent with the hypothesis,
should be interpreted with caution. It is possible that there is a ceiling effect
for anger that reduces its discriminating power, as the modal response for both
emotional targets is 5, the maximum score. In addition, we are discussing the
results of a single, cross-sectional study. For this reason, it is necessary to
replicate these findings.
PRIDE, ANGER AND CROSS-CUTTING TALK
11
Results
An ordinal regression, this time with a negative log–log function (as recommended when lower categories are more probable than higher categories), was
estimated predicting the extent of disagreement in individuals’ political discussion networks measured in September, 2008, based on emotional reactions
to both Democratic and Republican candidates in February of that year,
controlling for a number of variables gauged in January, 2008. The results
are displayed in Table 2 below.
As hypothesized, there is a positive, significant association between Wave
2 pride toward both Clinton (B ¼ .066, SE ¼ .032, p < .05) and McCain
(B ¼ .071, SE ¼ .030, p < .05) and Wave 9 discussion disagreement. As for
the relationship between feelings of anger and discussing politics with opposition networks, all three individual tests turn out to be insignificant, in
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opinions were from their own views (for up to three discussants) using a
5-point ordinal scale (reversed, so that 1 ¼ not different at all and 5 ¼ extremely
different). Responses to these questions were added across all discussants,
recoding those with no discussants as zero (range ¼ 0–14, M ¼ 4.59,
SD ¼ 3.55, skewness ¼ .10).
Following the temporal ordering of the variables implied in the hypotheses, all independent variables used in this study were measured in Waves 1
(January, 2008) and 2 (February, 2008). For the variables of emotion, respondents were asked how ‘‘proud’’ and ‘‘angry’’ they felt when they thought about
then candidates Hillary Clinton, Barack Obama, and John McCain (chosen
because they led the primary season polls). Responses were recorded on a
5-point scale (reversed, ranging from 1 ¼ not at all to 5 ¼ extremely). To
make the results comparable with Study 1, emotions were gauged separately
for each emotional target (Obama pride: M ¼ 2.45, SD ¼ 1.44; Obama anger:
M ¼ 1.64, SD ¼ 1.17; Clinton pride: M ¼ 2.25, SD ¼ 1.40; Clinton
anger: M ¼ 2.09, SD ¼ 1.44; McCain pride: M ¼ 2.30, SD ¼ 1.26; McCain
anger: M ¼ 1.59, SD ¼ 1.01).
Control variables were similar to those of Study 1, and were all but one
measured in Wave 1. Demographics included age in years (M ¼ 47.37,
SD ¼ 16.87), gender (51.89% female), and education (M ¼ 2.88, SD ¼ 1.12).
For political interest, respondents were asked how interested they were in
‘‘information about what’s going on in government and politics’’ (M ¼ 3.52,
SD ¼ 1.08). Ideology was measured with the standard 7-point party identification scale used in the United States (M ¼ 2.96, SD ¼ 2.11). For television
news exposure, participants were asked how many days during a typical week
they watched news on television, not including sports (M ¼ 4.79, SD ¼ 2.30).
Network size, gauged in Wave 9, was a straightforward measure of the number
of discussants named by respondents (up to eight) (M ¼ 4.26, SD ¼ 3.20).
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Table 2
Predicting Disagreement in Social Networks (Study 2, United States, Multi-Wave Panel
Data)
DV: (Wave 9) In general, how different are ALTER’s
[added 0 to 3 ALTERS] opinions about government and
elections from your own views?
Predictors
Pride (W2)
Anger (W2)
Age
Education
Gender (female)
Political interest (W1)
Political ideology (W1)
Television news
exposure (W1)
Network size (W9)
Nagelkerke R2
N (weighted)
Model 2
Emotional target:
Hillary Clinton
B
(SE)
B
(SE)
.016
.033
.002
.000
.190**
.036
.017
.029
(.030)
(.034)
(.002)
(.033)
(.072)
(.039)
(.018)
(0.18)
.066*
.010
.002
.006
.194**
.035
.008
.032
(.032)
(.030)
(.002)
(.032)
(.072)
(.040)
(.021)
(.018)
.317***
(.015)
.492
1,079
.318*** (.015)
.493
1,077
Model 2
Emotional target:
John McCain
B
.071*
.017
.003
.005
.172*
.029
.042*
.028
(SE)
(.030)
(.036)
(.002)
(.032)
(.071)
(.041)
(.017)
(.018)
.317*** (.015)
.492
1,072
Note. Cell entries report coefficients (B) and standard errors (SE) from ordinal regression analysis with a
negative log–log function (as recommended when lower categories are more probable than higher categories). W1 ¼ Wave 1, W2 ¼ Wave 2, W9 ¼ Wave 9. DV = Dependent variable.
*p < .05, **p < .01, ***p < .001 (two-tailed).
response to RQ1. However, contrary to H1, Obama pride is not associated
with subsequent cross-cutting talk.
Discussion
The results of Study 2 validate the findings of Study 1, in that feelings of
pride were a significant positive predictor of engaging in cross-cutting discussion in two of three tests, whereas feeling angry toward the presidential candidates of the 2008 U.S. election was not. There greatest strength of Study 2
compared with Study 1 is that we were able to address the relationship between emotions and disagreement in a more conservative manner, as the emotion variables were measured before—not concurrently—with the disagreeable
discussion variable. Nevertheless, there are a number of additional differences
in design between Study 1 and Study 2. For that reason, it is remarkable to
find a similar pattern of relationships. Unlike Study 1, we analyzed data
collected during an election, a context that is favorable to triggering political
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Model 1
Emotional target:
Barack Obama
PRIDE, ANGER AND CROSS-CUTTING TALK
13
Study 3
Method
This study was based on a survey fielded in 2008 in the context of a national
referendum initiative in Switzerland about the naturalization of immigrants.
The initiative proposed a stricter application and decision processes for immigrants who seek Swiss citizenship, but it was rejected by a majority of
voters. A two-wave, computer-assisted telephone interview panel survey was
conducted. The first wave was fielded by a professional polling company in
April 2008 (N ¼ 1,251), for a response rate of 9%. The second wave took
place right after the vote on June 1, 2008 (N ¼ 999). The sample was recruited
applying a random-quota procedure and is representative of Switzerland’s
population in terms of sex, age, education, and residence (for additional details
about the survey, see Matthes, 2012; Schemer, Wirth and Matthes, 2012).
Variables
The dependent variable, discussion disagreement, was measured in a general
fashion, as in Study 1. Respondents were asked how frequently they discussed
the naturalization issue with persons with whom they do not share the same
opinion about it (range 1 ¼ very seldom to 5 ¼ very often; wave 1: M ¼ 2.21,
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emotions and engaging in political discussions, both of which may affect the
frequency of cross-cutting talk. Additionally, the current study uses data based
on ego-centered networks and name generators, whereas in Study 1, the data
are based on general assessments of discussion networks. There are cultural
differences, too. A comparison of the distribution of disagreeable talk between
Chilean and American samples yields that disagreement is more common in
the former. To the degree that there are stable, cross-country differences in
the frequency with which people engage in cross-cutting exposure within their
social networks, short-term factors such as emotional reactions could have a
different impact in settings where disagreement is more or less frequent. Yet,
the results of both studies are quite consistent. The notable exception is the
result for Obama pride, a finding for which—at this stage—there is no clear
explanation beyond speculation about the exceptionality of him being the first
African American candidate with serious chances of becoming president.
Still, the insights gained from Study 2 are limited by the fact that we
cannot control for prior levels of disagreement. In addition, the effect of
emotions on discussion may be short lived, making less credible the
6-month span between the measurement of emotions and their impact on
disagreeable talk. Hence, we conducted a third study that somewhat alleviates
these concerns.
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INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH
Results
Two ordinal regression models with a negative log–log function were estimated. The first model is similar to Study 2, in that Wave 2 disagreement was
regressed on Wave 1 emotions plus controls. The second model is more
stringent in terms of causal inference, as it incorporates Wave 1 disagreement
as a control. Thus, the second model—in contrast to Studies 1 and 2—
predicts change in discussion disagreement based on emotional reactions
(Finkel, 1995).
As shown in Table 3, Model 1, pride toward foreigners in Wave 1 is
associated to higher levels of disagreement in Wave 2 (B ¼ .098, SE ¼ .034,
p < .01). This finding is replicated in Model 2, which shows that pride is a
positive predictor of an increase in discussion disagreement across waves
(B ¼ .087, SE ¼ .035, p < .05). These findings provide strong support for
H1. Anger, on the other hand, does not exhibit a significant relationship
with cross-cutting talk (model 1, B ¼ .034, SE ¼ .035, n.s.; model 2,
B ¼ .019, SE ¼ .035, n.s.).
Discussion
The results of the current study basically replicate the pattern found in Studies
1 and 2, suggesting that pride can motivate frequent political discussions with
people who have alternatives viewpoints in a way that anger cannot. This time,
however, we used a more robust, causal specification, where we predict not only
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SD ¼ 1.20, skewness ¼ .66; Wave 2: M ¼ 2.25, SD ¼ 1.08, skewness ¼ .53). To
make the results of this study comparable with Studies 1 and 2, two sets of
emotional reactions were measured in Wave 1, with participants asked to
indicate how much they agreed with statements describing how they felt
toward immigrants (ranging from 1 ¼ do not agree at all to 5 ¼ totally agree):
pride (M ¼ 2.71, SD ¼ 1.18) and anger (M ¼ 2.56, SD ¼ 1.24).
The control variables were all measured in Wave 1, except for one variable. Demographics included age in years (M ¼ 48.52, SD ¼ 16.84), educational attainment (M ¼ 7.02, SD ¼ 3.18), and gender (51.32% female).
Political interest was a single item asking how interested in politics in general
are respondents (M ¼ 3.02, SD ¼ .76). For ideology, respondents were asked
to place themselves on an 11-point, left-right wing scale (M ¼ 6.16,
SD ¼ 2.19). Television news use was operationalized by asking respondents
about the importance of television as means for following news about the
referendum campaign (M ¼ 3.83, SD ¼ 1.24). Finally, network size was operationalized from an item asked in Wave 2, in which participants reported in
open-ended fashion how many people did they talk about the referendum in
the past 2 months (logged, M ¼ .98, SD ¼ .37).
15
PRIDE, ANGER AND CROSS-CUTTING TALK
Table 3
Predicting Disagreement in Social Networks (Study 3, Switzerland, Two-Wave Panel
Data)
DV: (Wave 2) Generally, how often you discuss with
persons that do not share your opinions about the issue
of immigration?
Predictors
Pride (W1)
Anger (W1)
Age
Education
Gender (female)
Political interest (W1)
Political ideology (W1)
Television news
exposure (W1)
Network size
(logged; W1)
Lagged DV
Nagelkerke R2
N
Model 2
Emotional target:
Immigrants
B
(SE)
.098**
.034
.003
.010
.110
.110
.002
.058
(.034)
(.035)
(.002)
(.013)
(.085)
(.059)
(.019)
(.034)
1.458***
(.123)
B
(SE)
.087*
.019
.003
.001
.087
.085
.0001
.046
(.035)
(.035)
(.002)
(.013)
(.085)
(.059)
(.019)
(.034)
1.300***
(.127)
.240***
.158
938
(.035)
.251
928
Note. Cell entries report coefficients (B) and standard errors (SE) from ordinal regression analysis with a
negative log–log function (as recommended when lower categories are more probable than higher categories). W1 ¼ Wave 1, W2 ¼ Wave 2, DV ¼ Dependent variable.
*p < .05, **p < .01, ***p < .001 (two-tailed).
levels of disagreement but also change in levels of disagreement. Again, the
specific context of this study—a referendum campaign—does not seem to alter
the relationships already established in Studies 1 and 2 regarding nonelectoral
and electoral settings. To further explore the causality quandary, we conducted
a post hoc analysis of reverse causality (available on request), in which Wave 1
disagreement is a predictor of Wave 2 emotions, controlling for Wave 1 emotions and the rest of control variables described above. This analysis identified
no discernible relationship between disagreeable talk and pride or anger. Thus,
Study 3 provides better evidence of a one-way causal effect of feelings of enthusiasm on cross-cutting talk than the two previous studies.
General Discussion
Feelings toward political issues and public figures are one of myriad factors
that determine public opinion formation, expression, and mobilization. Other
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Model 1
Emotional target:
Immigrants
16
INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH
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factors, such as resources and media use, often determine cross-cutting exposure in social networks. And for many citizens, the political world does not stir
their emotions at all; if anything, it triggers apathy and indifference. Yet, there
is consistent evidence showing that political stimuli frequently elicit a variety
of positive and negative feelings on citizens. The current research is an initial
attempt to elaborate on the individual-level consequences of pride and anger
on the likelihood of encountering disagreement in discussion networks.
Borrowing from cognitive and appraisal theories, and relying on an emotions-as-information approach (Lerner & Keltner, 2000; Nabi, 1999), this
study has shown that pride is a significant predictor of disagreeable talk
even when controlling for network size, political interest, media use, resources,
and ideology. Proud individuals are thus more likely to expose themselves to
disagreement within their social networks. Anger, however, has no such effects, that is, angry individuals are equally likely to seek and avoid crosscutting talk.
Because the results stem from a secondary analysis of national surveys,
their validity depends on accepting that key variables are comparable across
studies. In Studies 1 and 3, disagreement was operationalized with a summary
measure of network opposition, whereas Study 2 uses the name-generator
approach. Although alternative measures of divergence in discussion networks
can lead to different conclusions, our conceptualization of disagreement is
consistent across studies. We do not use party identification or vote choice
for defining whether individuals’ networks are oppositional (cf., Huckfeldt et
al., 2004; Mutz, 2006). Rather, we focus on sheer volume of disagreement.
In a similar vein, and despite differences in emotional targets and question
wording, in all three studies, the same affective states were gauged (i.e., pride
and anger). In Study 1, we captured separately emotions toward an environmental issue and the presidential figure. In Study 2, the targets were the
Republican and Democratic candidates vying for their party’s presidential
nomination. In Study 3, the emotion variables were related to immigrants.
Finding a similar pattern of effects, with somewhat different variable operationalization, should bolster confidence that our measures indeed are tapping
subjective individual emotional states. Further, in Study 3, we measured both
level and change in emotions within a 2-month time-span, which is consistent
with our conceptualization of affective variables as short-term factors rather
than long-standing reactions.
The main takeaway of the current research, then, is that certain specific
emotions—in this case, pride—can inform people’s decision to expose themselves to disagreeable political talk. This could be an effort by people to
reinforce their feelings, or a consequence of having the emotional resources
to endure disagreement in their discussion networks. We do not know if this is
the result of a conscious decision, but it does suggest that proud individuals
PRIDE, ANGER AND CROSS-CUTTING TALK
17
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are capable of engaging in a socially risky and costly action such as discussion
with people with divergent perspectives. There is also the possibility that for
some people, political conflict is a fun and pleasant activity, or it makes them
feel better about themselves. In this case, feelings of pride, as well as joy and
enthusiasm, could be sustained through cross-cutting exposure. Future research could further elucidate the possibility of reciprocal relationships between emotion and cross-cutting talk.
To date, only two studies have examined in detail the relationship between
emotions and disagreeable discussion (Lyons & Sokhey, 2014; Parsons, 2010).
While one of them has entertained the possibility that emotions make more or
less likely engagement in discussion with opposing viewpoints (Lyons &
Sokhey, 2014), it does not examine the role of anger and, furthermore, conflates the role of pride with hope—an emotion with different characteristics
and ramifications (see Lazarus, 1991). The other study (Parsons, 2010) reverses the direction of causality, positing that cross-cutting talk triggers political emotions. However, both our data and Lyons and Sokhey’s (2014, pp.
248–249) do not support this possibility. In either case, both works are based
on AIT and a dimensional view of emotions, severely limiting their ability to
examine in isolation the roles played by pride and anger. As Nabi (2010,
pp. 153–154) argued, a discrete emotion approach ‘‘goes much further by
capturing the additional elements that provide the nuance necessary to explain’’ the complexity of communication processes and effects.
As in any other study, there a number of limitations that future research
could address. The emotion measures use single items and, thus, have an
unknown degree of measurement error. Including additional emotions would
also be desirable, as it would further substantiate our claim that it is discrete
emotions what matters, not just positive or negative affect. Using three
waves of data, instead of two, would allow to better separate reliability
from stability of variables across time. An alternative operationalization of
cross-cutting talk, such as competitive or mixed composition of networks,
may yield different results from those reported. The current research is also
moot regarding the mechanisms that intervene between emotions and
discussion.
These limitations notwithstanding, the strengths of this article lie in its
novel analysis of the relationship between disagreement and emotions, as well
as the cross-country replications. To recapitulate, Study 1 provides initial
evidence of the associations between pride, anger, and disagreement. Study
2 replicates this relationship but is more conclusive about of the temporal
ordering of the variables. Study 3 addresses causality more directly, as it enables us to estimate a conditional change model (Finkel, 1995). Thus, each
study builds on the limitations of the previous one and, collectively, provides
solid evidence of an affective route toward cross-cutting exposure, in line with
18
INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH
functional models of emotion (Forgas, 1995; Han et al., 2007; Nabi, 2002).
Further exploration of these phenomena promises to enrich our understanding
of informal political conversations.
Funding
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Biographical Notes
Sebastián Valenzuela (Ph.D., University of Texas at Austin) is assistant professor in
the School of Communications at Pontificia Universidad Católica de Chile. His main
research areas are political communication, journalism, and social media.
Ingrid Bachmann (Ph.D., University of Texas at Austin) is assistant professor in the
School of Communications at Pontificia Universidad Católica de Chile. A former
reporter and blogger, her research interests include news narratives, gender, and
political communication.
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