The first presidential debate of 2016 was historic along a number of dimensions, including the first woman general election candidate and the first general election candidate in history with no political or military experience. Given the presence of the first woman nominee of a major party, along with dramatic gender differences in support for the candidates, we focus on the role of gender in shaping people’s emotional responses to candidate messaging during the debate. Through the use of a controlled experiment, we measure changes in attitudes after exposure to the debate. In addition, we utilize facial expression software to explore real-time reaction to the candidates during the debate. Leveraging data generated during the debate by the facial expression software and as well as responses to pretest and post-test questionnaires, we find that men and women respond differently to candidates’ messaging during the debate and these emotional responses influence post-debate evaluations.
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The gender gap figures come from a Washington Post-ABC News poll conducted between September 5 and 8, 2016 among a random national sample of 1002 adults. https://www.washingtonpost.com/apps/g/page/politics/washington-post-abc-news-national-poll-sept-5-8-2016/2090/.
See also Roseman et al. (2013).
In contrast, Kemper’s structural theory of emotion (Kemper 1978) theorizes that individuals’ social position, rather than cultural norms, influence people’s emotional responses in specific social situations. In many situations, emotions predicted by cultural norms overlap with the emotions predicted by social positions.
Different emotions produce divergent reactions from people. For instance, anger often induces political participation and may lead voters to take risks, while fear may encourage voters to look for new information, withdraw from action, or avoid risks (see, for example, Huddy et al. 2015; Valentino et al. 2011; Valentino et al. 2018).
The first presidential debate did not include opening or closing statements by the candidates. The debate began with Lester Holt introducing the first segment, “Achieving Prosperity.” A transcript of the entire debate can be found here: https://www.npr.org/2016/09/26/495115346/fact-check-first-presidential-debate?utm_source=twitter.com&utm_medium=social&utm_campaign=npr&utm_term=nprnews&utm_content=20160926.
This study was part of larger study where 177 subjects were recruited, with 91 subjects randomly assigned to the debate condition and 86 assigned to the control condition.
For subjects at the southeastern campus, subjects were told that a webcam would be recording their facial expressions during the debate, while subjects on the southwestern campus were not told explicitly that their facial expressions would be recorded during the debate. During the debriefing, subjects on the southwestern campus were told about the recording of their facial expressions.
At the southwestern university, students completed the survey, via Qualtrics, on their desktop computers. At the southeastern university, subjects completed a pencil and paper questionnaire. At the completion of the study, students at the southwestern university received the following debriefing: “Thank you for your participation in this research study. The goal of this study is to determine how people react to presidential debates. To examine the impact of the presidential debate, people were randomly assigned to watch the debate or watch a non-political video. During the time that you watched the debate, the video camera on the computer delivering the video recorded your facial reactions. We are using a computer program to extract your emotional reactions to the video and after the emotional data is extracted, we will delete the video recorded during your experimental session. The facial data will never be associated with your name. Again, thank you for your participation”.
The video data collected by each webcam was set to a frame rate of 30 frames per second and a camera resolution of 640 × 480 pixels.
Students were not exposed to any commentary from PBS anchors. The viewing of the debate began with the moderator’s introductory comments.
In Appendix B of ESM, we present the exact question wording for the measures examined in this paper.
The most systematic large scale investigation of FACS reliability in spontaneous facial behavior was done by Sayette et al. (2001). In this study, the researchers induced changes in facial expression by using emotion inductions (e.g., olfactory stimulation). The researchers found that high levels of reliability for all but two action units. In addition, concurrent validity has been established by comparing manual FACS coding with computer based approaches (e.g., Cohn et al. 2007; Pantic and Patras 2006.).
While Emotient FACET extracts two complex emotions (i.e., frustration and confusion), we do not include these advanced emotions in our analysis. We present a screen shot of the Emotient FACET display in Figure A1 in the Appendix A of ESM.
See Chaplin and Aldao (2013)for a review of internalizing and externalizing emotions.
See https://help.imotions.com/hc/en-us/articles/205256321-FACET-FAQ. Given the content and format of the debate, we do not anticipate dramatic emotional reactions, as we might expect if subjects are playing a video game or watching an action movie. Therefore, we have measured the micro-expressions of emotion that are more commonly displayed in response to content like the debates by setting the Emotient FACET threshold at 0.05.
Three distinct theories have been advanced (i.e., the valence theory, the discrete theory, the appraisal theory) to study the role of emotions (see Marcus 2003 for a review). In this paper, we choose a discrete approach because we are interested in exploring the expression and impact of specific internalizing and externalizing emotions.
The complete data set and questionnaires are available via ICPSR. Replication code for the analyses presented here are available via the Political Behavior Dataverse: https://dataverse.harvard.edu/dataverse/polbehavior).
These moments were chosen a priori to represent different policy areas as well as moments corresponding to each candidate’s strengths and weaknesses (e.g., Hillary Clinton on the email scandal; Donald Trump on releasing his taxes). Given constraints of the software, it was necessary to examine short periods of time (i.e., less than 10 min) rather than the entire debate. The eight moments range in time from 6 minutes and 26 seconds for Moment #3 to 8 minutes and 53 seconds for Moment #2 (see Appendix C of ESM). These 8 moments are the only time periods where we have collected the respondents’ emotional reactions to the ninety-minute debate. Looking across the moments of the debate, people appear to react in meaningful ways to changes in the content of the debate. For instance, expressions of contempt are highest among men and women when Donald Trump is defending his role in the birther controversy (i.e., Moment #5), while expressions of disgust are lowest when both candidates discuss their plans for the combatting ISIS (i.e., Moment #6).
For more discussion of the Emotient FACET technology, see Appendix D of ESM.
Although 91 subjects participated in the emotions debate conditions, we have complete emotion data for 66 of the 91 subjects. Subjects may be missing from the emotions dataset because their faces may have become obscured during the debate or because they were sitting too far from the computer at different times during the debate. We should note that most studies utilizing software and hardware to automatically code facial expressions utilize small sample sizes. For example, Fasel and Luettin (2003) review studies relying on automatic facial expression analysis and find that the number of subjects in these studies ranged from 8 to 40.
Fear is expressed, on average, in 1% of the frames (with a standard error of 0.8), while anger is expressed an average of 6% of the frames (with a standard error of 1.4), contempt is expressed, on average, in 12% of the frames (with a standard error of 1.5), disgust is expressed, on average, in 3% of the frames (with a standard error of 0.60) and sadness is expressed in 6% of the frames, on average (with a standard error of 1.4).
We can compare expressions of sadness, anger and disgust (i.e., the emotions where we find significant gender differences) for men and women who identify as Democrats and for men and women who identify as Republicans. Beginning with Democrats, we find women are more likely to express sadness, averaging expressions of sadness in 8.22 frames, compared to 4.45 frames for men, while men express anger (an average of 7.44 frames) and disgust (an average of 5.13 frames) more often than women (an average of 2.89 frames for anger and an average of 1.51 frames for disgust). The number of respondents in each group is small, making it difficult to achieve statistical significance. (The gender differences in expressions of disgust do reach statistical significance at p < 0.05). Turning to Republicans, men express more anger (an average of 15.95 frames) and disgust (an average of 4.96 frames) than women (an average of 2.54 frames for anger and an average of 1.48 frames for disgust), but men and women express similar expressions of sadness (an average of 4.22 frames for men and 3.14 frames for women). For Republicans, the differences between men and women fail to achieve statistical significance. Finally, we examine whether the gender differences in emotional responses persist when we control for the party of the respondent. In particular, we estimated a series of OLS regressions predicting the five different emotions, including gender and partisanship as independent variables. In this analysis, party fails to achieve statistical significance in any of the five models. In contrast, gender continues to significantly influence people’s expressions of anger and disgust, but fails to achieve statistical significance in the model predicting expressions of sadness (although the coefficient is positive, indicating that women are more likely to express sadness). In additional analyses, we reestimated the OLS regressions predicting the five emotions with partisanship, gender, and an interaction between gender and partisanship. In this multiplicative analysis, the interaction term failed to reach statistical significance in any of the five models. We estimated party in two ways; democrats = 1, republicans = 0 (excluding independents) and democrats = 1, independents = 0, republicans = − 1 and the results are unchanged.
Again, we examine whether gender differences in emotional expressions across the 8 moments persist when we control for partisanship. In particular, we look at whether the gender differences in expressions of sadness, anger and disgust persist when we compare emotional expressions within Democratic respondents and within republican respondents for each of the 8 moments. Among democrats, we find that women express more sadness than men for each of the 8 moments (although these differences fail to reach statistical significance for any of the eight moments). Democratic men express more anger and disgust than Democratic women for each of the eight moments and these differences reach statistical significance three times for anger and four times for disgust. Among Republicans, we find that women express more sadness than men in only 3 of the 8 moments (although none of the gender differences reach statistical significance for any of the eight moments). Republican men express more anger than Republican women for each of the eight moments, with the difference reaching statistical significance for one of the moments. Finally, Republican men express more disgust than Republican women for 7 of the 8 moments, reaching statistical significance for one of the moments.
In Appendix C of ESM, we present the proportion of time each candidate is speaking for each of the eight moments. Clinton spoke more that Trump in only one of the 8 moments during the debate (moment 4, during which the discussion primarily concerned implicit racial bias).
See Appendix C of ESM for a full transcript of each of the moments in the debate.
See Appendix B of ESM for exact question wording of the dependent variables and see Table A2 for a detailed description of the measures and summary statistics.
See Brambor et al. (2005) for a discussion of how to properly specify interaction models.
We eliminated the interaction term between sadness and gender because of high levels of multicollinearity. In the models predicting Trump’s debate performance and Clinton’s debate performance, VIF is greater than 10 and the tolerance is less than 0.10 for sadnesss and sadness × gender, indicating high levels of multicollinearity between these component terms.
Low values are set a 0 and high values are one standard deviation above 0. In estimated the interaction effects, we place all remaining variables at their mean. In addition, the dummy variable for race is set to 0.
Since displays of emotions cannot be connected to a specific candidate (e.g.,, we cannot tell whether people are showing disgust when Trump is talking or when Clinton is talking), we cannot be sure if women are disgusted by Clinton or Trump. We do know that levels of disgust for women are not related to the amount of time that Trump or Clinton is talking during the moments during the debate (see Fig. 1). Our findings displayed in Fig. 2c may reflect that (1) women with high levels of disgust are disgusted by Clinton and, therefore, expressions of disgust produce positive views of Trump’s debate performance or (2) women’s expressions of disgust are produced by their disgust that Trump is doing well in the debate and therefore, high levels of disgust are associated with positive assessments of Trump’s debate performance.
Assessments of debate performance are electorally relevant. We find a significant relationship between assessments of Trump’s debate performance and people’s intended vote choice as indicated on the post-debate questionnaire (1 = intend to vote for Clinton, − 1 intend to vote for Trump and 0 = intend to vote for someone else) with a χ2 (16, n = 88) = 45.64, p < 0.01. We also find a significant relationship between assessments of Clinton’s debate performance and intended vote choice with a χ2 (14, n = 88) = 31.91, p < 0.01.
In the OLS regression predicting post-debate feeling thermometer scores for Trump, the unstandardized coefficient for Trump’s debate performance ratings is 4.87 with a standard error of 0.80 (p < 0.01). In the OLS regression predicting post-debate feeling thermometer scores for Clinton, the unstandardized coefficient for Clinton’s debate performance is 3.13 with a standard error of 0.96 (p < 0.01).
As before, we center all of the non-dichotomous independent variables (with the exception of feeling thermometer scores at T1) in our analysis to reduce multicollinearity (Aiken et al. 1991) and increase the ease of interpretation (Williams 2015). And, again, the results do not changed markedly when we do not center these variables.
Low values of each emotion are set a 0 and high values are one standard deviation above 0. In estimating the interaction effects, we place all remaining variables at their mean. In addition, the dummy variable for race is set to 0.
Once again, it was necessary for us to eliminate the interaction term between sadness and gender because of high levels of multicollinearity. In the models predicting changes in evaluations of Trump and Clinton, VIF is greater than 10 and the tolerance is less than 0.10 for sadnesss and sadness*gender, indicating high levels of multicollinearity between these component terms. In the model predicting changes in feeling thermometer scores for Clinton (pre-debate to post-debate), we find that people who feel sadness become less favorable in their views towards Clinton after watching the debate. Feelings of sadness are not related to changes in people’s impressions of Trump.
As the data in Table 4 shows, the interaction coefficient for anger, while positive, does not reach statistical significance.
The dependent variable is coded 1 for people who say they will vote for Clinton, − 1 for people who say they will vote for Trump, and 0 for people who prefer an third party candidate or do not have a preference. See Appendix B of ESM for exact question wording.
The parameter estimate for Clinton feeling thermometer scores (at the posttest) is 0.08 (with a standard error of 0.02, p < 0.01) and the parameter estimate for Trump feeling thermometer scores is -0.05 (with a standard error of 0.02, p < 0.01). Party identification, gender and race fail to reach statistical significance.
We do not look at changes in vote preference because 79.5% of the sample do not change their vote preference from the pretest questionnaire to the posttest questionnaire.
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An earlier version of this paper was presented at the 2017 American Political Science Association Meeting. The data collection for this project was supported by the National Science Foundation under Grant No. 1650397. We would like to thank the following students who helped with the debate experiments: Zachary Arlington, Ryan Deutsch, Joshua Galvan, Sammy Goldenberg, Micah Kyler and especially Manny Gutierrez. Abigail Bowen, Bailey Fairbanks, Chanel Harley, Liam Hayes, Justin Kingsland, Matthew Montgomery, Reagan Griggs Prichett, Abdelrahman Rashdan and Adnan Rasool.
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Fridkin, K.L., Gershon, S.A., Courey, J. et al. Gender Differences in Emotional Reactions to the First 2016 Presidential Debate. Polit Behav (2019). https://doi.org/10.1007/s11109-019-09546-9
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