Pride, anger, and cross cutting talk: A three country study of emotions and disagreement in informal political discussions
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(2) 2. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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..
(3) 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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..
(4) 4. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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..
(5) PRIDE, ANGER AND CROSS-CUTTING TALK. 5. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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.
(6) 6. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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,.
(7) 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.. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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?.
(8) 8. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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%..
(9) 9. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. Model 1 Emotional target: HidroAysén.
(10) 10. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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..
(11) 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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)..
(12) 12. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. Model 1 Emotional target: Barack Obama.
(13) 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,. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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..
(14) 14. 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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) 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. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. Model 1 Emotional target: Immigrants.
(16) 16. INTERNATIONAL JOURNAL OF PUBLIC OPINION RESEARCH. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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.
(17) PRIDE, ANGER AND CROSS-CUTTING TALK. 17. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. 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) 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. References Albarracı́n, D., & Mitchell, A. (2004). The role of defensive confidence in preference for proattitudinal information: How believing that one is strong can sometimes be a defensive weakness. Personality and Social Psychology Bulletin, 30, 1565–1584. doi: 10.1177/0146167204271180 Bachmann, I., Correa, T., & Gil de Zúñiga, H. (2012). Profiling online political content creators: Advancing the paths to democracy. International Journal of E-Politics, 3, 1–19. doi: 10.4018/jep.2012100101 Borah, P., Edgerly, S. Vraga, E., & Shah, D. (2013). Hearing and talking to the other side: Antecedents of cross-cutting exposure in adolescents. Mass Communication and Society, 16, 391–416. doi: 10.1080/15205436.2012.693568 Brader, T. (2006). Campaigning for hearts and minds: How emotional appeals in political ads work. Chicago, IL: University of Chicago Press. Bushman, B. J. (2002). Does venting anger feed or extinguish the flame? Catharsis, rumination, distraction, anger, and aggressive responding. Personality and Social Psychology Bulletin, 28, 724–731. doi: 10.1177/0146167202289002 Buttice, M., Huckfeldt, R., & Ryan, J. B. (2009). Polarization, attribution and communication networks in the 2006 congressional elections. In J. J. Mondak & D. Mitchell (Eds.), Fault lines: Why the Republicans lost congress (pp. 42–60). New York, NY: Routledge. Chaffee, S., McLeod, J., & Wackman, D. (1973). Family communication patterns and adolescent political socialization. In J. Dennis (Ed.). Socialization to Politics (pp. 349–356). New York, NY: Wiley. DeBell, M., Krosnick, J. A., & Lupia, A. (2010). Methodology report and user’s guide for the 2008-2009 ANES Panel Study. Retrieved from http://electionstudies.org/ studypages/2008_2009panel/anes2008_2009panel_MethodologyRpt.pdf. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. The authors thank funding support received from the Office of the Vice President for Research (VRI Inicio grants 1/2012 and 17/2012) at Pontificia Universidad Católica de Chile during the writing of this manuscript. The first author also received support from CIGIDEN (grant CONICYT/Fondap/ 15110017). Any opinions, findings, conclusions, or recommendations expressed are, of course, those of the authors only. Jörg Matthes and the NCCR Democracy as well as Andrés Scherman and the School of Journalism at Universidad Diego Portales generously shared their data with the authors..
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(21) PRIDE, ANGER AND CROSS-CUTTING TALK. 21. 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.. Downloaded from http://ijpor.oxfordjournals.org/ at Pontificia Universidad Cat�lica de Chile on May 16, 2016. Valenzuela, S., Kim, Y., & Gil de Zúñiga, H. (2012). Social networks that matter: Exploring the role of political discussion for online political participation. International Journal of Public Opinion Research, 24, 163–184. doi: 10.1093/ijpor/ edr037 Vorderer, P. & Hartmann, T. (2009). Entertainment and enjoyment as media effects. In J. Bryant and M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd edn., pp. 532–550). New York, NY: Routledge. Yez, L. (2013). Desafı́os éticos de la cobertura televisiva de un hecho traumático [Ethical challenges on television coverage of a traumatic event]. Cuadernos.info, 32, 39–46. doi: 10.7764/cdi.32.494.
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