Abstract
Although few studies have explored the link between emotion and political talk, here I argue that political disagreement depolarizes emotional reactions via information exchanged in social networks. Analyzing data from the ANES 2008–2009 Panel Study, several conclusions are drawn. First, disagreement increases negative emotions and decreases positive emotions toward the in-party candidate, and also increases positive emotions and decreases negative emotions toward the out-party candidate. In other words, disagreement depolarizes emotions toward political candidates. Second, the affective impact of disagreement does not vary with political knowledge. Finally, positive emotions toward the out-party candidate and negative emotions toward the in-party candidate reduce political interest, candidate issue placement accuracy, and political participation. Overall, this study develops important theoretical connections between affect and political talk that have implications for the value of political disagreement.
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Notes
Nonverbal behavior, such as facial expressions and body language, has been found to greatly contribute to emotional response (Ekman and Rosenberg 1998; Masters and Sullivan 1989). It is possible that the content of information does not elicit emotional response to the same degree as nonverbal expressions.
An alternative view may be that anxiety or anger motivates people to seek out additional information and different opinions in their social networks. However, previous work shows that people generally try to avoid the discomfort of cross-cutting exposure (Berelson et al. 1954; Festinger 1957; Mutz 2006) by constructing more homogeneous communication networks (Huckfeldt et al. 1995; Huckfeldt and Sprague 1987). Therefore, exposure to new information via disagreement may be a function of involuntary social interactions, such as those found in the workplace (MacKuen 1990; Mutz and Mondak 2006).
Although a nominal three-point party identification scale is used in the following analyses to test the depolarization hypotheses, I acknowledge the potential impact of partisan strength. The strength of partisan identification may either moderate or alter the emotional impact of political disagreement. For instance, a strong Republican exposed to disagreement may instead react with anger (aversion) toward an out-party or Democratic candidate and greater enthusiasm toward an in-party or Republican candidate. Alternatively, a weak Republican may react to disagreement with fear or anxiety toward a Republican candidate and more enthusiasm for a Democratic candidate. In other words, partisan strength may determine whether disagreement produces partisan polarization (strong partisans) or partisan depolarization (weak partisans) of emotional reactions in social networks.
It is important to note that the emotional effects of political agreement may produce an opposite pattern, reflecting partisan polarization. In other words, political agreement may decrease negative emotions and increase positive emotions toward an in-party candidate, and also increase negative emotions and decrease positive emotions toward an out-party candidate. Although this study largely focuses on the depolarizing effects of disagreement on emotion, I will return to the implications of partisan polarization in a later section.
These are data from the advance release (January 2009) of the 2008–2009 ANES Panel Study. DeBell and colleagues (2009) recommend that any conclusions drawn from these data be considered “tentative, preliminary, or subject to change” (p. 4). Additional information about the sampling methods and study design can be found in the User’s Guide to the Advance Release of the 2008–2009 ANES Panel Study (DeBell et al. 2009), which can be freely downloaded online.
Although previous work shows that respondent perceptions of discussant preferences tend to be fairly accurate (Huckfeldt 2001; Huckfeldt and Sprague 1995), I acknowledge the potential issues of endogeneity and selection bias that are common in social network research, particularly when relying entirely on self-report data. Experimental studies are generally preferred over observational studies in order to address issues of causality and endogeneity (McDermott 2002), and are increasingly being adopted in social network research (e.g. Ahn et al. 2007).
Marcus and colleagues (2006) recommend using feelings of hope, pride, interested, and elated to define enthusiasm and the disposition system; feelings of anxiety, fear, and worry to define anxiety and the surveillance system; and feelings of anger and disgust define the aversion dimension.
This measure is useful because it may encompass many potential aspects of disagreement, such as differences in policy issues, party identification, ideology, or candidate preference.
The index of political knowledge includes items that ask respondents about John McCain and Barack Obama, such as the states that they represent in Congress, their religion, and their previous job title.
Respondents were asked whether Barack Obama and John McCain favor/oppose/neither favor nor oppose a range of policy issues, such as e.g. same-sex marriage, taxes, prescription drug costs, and immigration laws. Correct responses to these items are given a one, while incorrect responses are given a zero. These are summed to create an index of issue placement accuracy, which ranges from a possible minimum of zero to a possible maximum of twenty-four. A complete list of the issue questions is available from the author and in the ANES Panel Study 2008–2009 User’s Guide (DeBell et al. 2009).
Respondents were asked if they have ever: (1) joined a protest march, rally, or demonstration, (2) attended a meeting of a town, city government, or school board, (3) signed a petition on the Internet about a political or social issue, (4) signed a petition on paper about a political or social issue, (5) given money to an organization concerned with a political or social issue, (6) attended a meeting to talk about political or social concerns, (7) invited someone to attend a meeting about political or social concerns, and (8) distributed information or advertisements supporting a political or social interest group.
As previously noted, although matching methods can be used to improve empirical claims about the relationship between two variables using observational data, issues of reciprocal causality and selection bias cannot be fully resolved with observational data. Experimental research is best suited to address issues of causality by carefully controlling treatment and control conditions (McDermott 2002).
While preprocessing these data using matching does not resolve issues of selection bias and endogeneity associated with social network research and the use of respondent self-reports, it does significantly minimize bias in model estimates as well as the extent to which the emotional impact of political disagreement depends on model selection and specification.
See Appendix Table 4 for diagnostics on the matching procedure. Media exposure is measured by averaging responses to survey items asking respondents how many days a week they get news from television, radio, newspaper, and Internet. Network education level is measured by averaging respondent answers regarding the highest education level completed by each discussant. Finally, ideology is measured on a standard seven-point scale. To simplify the matching process, media exposure and network education are dichotomized, while ideology is left unchanged.
The selected control variables are expected to influence the dependent variables (i.e. emotion) in the following ways. A respondent’s level of political knowledge may limit the impact of disagreement on emotion. The greater a respondent’s exposure to various media and new political information, the more likely an emotional response may result, hence the inclusion of media exposure. The level of educational attainment in social networks, perhaps a proxy for political expertise, may either enhance or limit the impact of disagreement depending on whether the distribution of preferences is homogeneous or heterogeneous. Finally, more liberal or conservative respondents may be more likely to experience strong emotional reactions toward certain candidates.
Previous works shows that nominal three-point and standard seven-point party identification scales both measure an underlying stable attachment to a political party (Cowden and McDermott 2000).
Additional analyses (not reported here) examined the effects of disagreement on emotions toward George W. Bush, which were also consistent with the depolarization hypotheses. Disagreement increased Republicans feelings of anger and fear toward Bush, and decreased feelings of hope and pride—and vice versa for Democrats.
Due to the ordered categories of emotional response, ordered logistic regression is used (Long 1997).
As previously indicated, the dichotomous variable for disagreement is an indicator of the treated and untreated respondents in the matching procedure. Also, political knowledge is split at its median value and separate models are estimated for low knowledge and high knowledge respondents.
See Appendix Table 4 for diagnostics on the matching procedures. The control variables include political disagreement, partisan identification, political knowledge, media exposure, and network political involvement; the latter is a combined measure of respondent perceptions of (1) how interested each discussant is in politics and (2) how likely each discussant will vote in November. The selected control variables are expected to influence the dependent variables (i.e. interest, issue accuracy, participation) in the following ways. Political knowledge, media exposure, and network involvement are all expected to enhance political interest, issue accuracy, and political participation. Finally, partisan identification is included to parse out the effect of emotion for different partisans.
It is important to note that the effect of emotion for Independents is notably mixed, for example, as feelings of anger and fear toward Obama decrease issue accuracy, while the same emotions toward McCain increase issue accuracy. If I were to speculate, perhaps these are election-specific effects due to the widespread negative misinformation campaign targeted at Independents that facilitated several rumors about Barack Obama.
An alternative interpretation of the results in Table 3 is that those who are less interested in politics, and less accurate in placing candidates on issues, are also going to be less committed to either candidate and thus more likely to experience a mixed series of emotional reactions toward both candidates. I acknowledge that this issue of reciprocal causality cannot be resolved using these data and that, as previously noted, an experimental approach can better isolate these causal mechanisms.
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Acknowledgements
I thank A.J. Barghothi, David Darmofal, Jennifer Jerit, Chris Zorn, three anonymous reviewers, and the panel participants at the 2007 Annual Meeting of the American Political Science Association for their excellent feedback on this project.
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Appendix
Appendix
Additional Robustness Checks
An important consideration regarding the selection of pretreatment control variables in matching procedures is to avoid what is known as “posttreatment bias,” that is, when control variables are affected by or consequences of the treatment variables (Ho et al. 2007a; King and Zeng 2007). Therefore, to provide additional robustness checks, I drop network education from the exact matching procedure for disagreement, and network political involvement and political disagreement from the exact matching procedures for emotion. I then recalculate means difference tests and ordered logit models to examine the extent to which the findings change with these potentially problematic control variables excluded from matching. In results not reported here, the findings do not change in direction or statistical significance, and thus the initial matching procedures that include these variables are reported in the text. However, the additional estimations and results are available from the author upon request.
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Parsons, B.M. Social Networks and the Affective Impact of Political Disagreement. Polit Behav 32, 181–204 (2010). https://doi.org/10.1007/s11109-009-9100-6
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DOI: https://doi.org/10.1007/s11109-009-9100-6