Edited by: Tracey Platt, University of Wolverhampton, United Kingdom
Reviewed by: Ulrich Von Hecker, Cardiff University, United Kingdom; Martina Raue, Massachusetts Institute of Technology, United States
This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology
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Raucous audience applause–cheering, laughter, and even booing by a passionately involved electorate marked the 2016 presidential debates from the start of the primary season. While the presence and intensity of these observable audience responses (OARs) can be expected from partisan primary debates, the amount of not just laughter, but also applause–cheering and booing during the first general election debate between Hillary Clinton and Donald Trump was unprecedented. Such norm-violating audience behavior raises questions concerning not just the presence, strength, and timing of these OAR, but also their influence on those watching on television, streaming video, or listening to radio. This report presents findings from three interconnected studies. Study 1 provides a baseline for analysis by systematically coding the studio audience response in terms of utterance type (laughter, applause–cheering, booing, and mixtures), when and how intensely it occurred, and in response to which candidate. Study 2 uses observational analysis of 362 undergraduate students at a large state university in the southern United States who watched the debate on seven different news networks in separate rooms and evaluated the candidates’ performance. Study 2 considered co-occurrence of OAR in the studio audience and in the field study rooms, finding laughter predominated and was more likely to co-occur than other OAR types. When standardized cumulative strength of room OAR was compared, findings suggest co-occurring OAR was stronger than that occurring solely in the field study rooms. Analysis of truncated data allowing for consideration of studio audience OAR intensity found that OAR intensity was not related to OAR type occurring in the field study rooms, but had a small effect on standardized cumulative strength. Study 3 considers the results of a continuous response measure (CRM) dial study in which 34 West Texas community members watched and rated the candidates during the first debate. Findings suggest that applause–cheering significantly influenced liking of the speaking candidate, whereas laughter did not. Further, response to applause–cheering was mediated by party identity, although not for laughter. Conclusions from these studies suggest laughter as being more stereotypic and likely to be mimicked whereas applause–cheering may be more socially contagious.
The 2016 election can be seen as one in which a passionately involved electorate was key for its unexpected outcome as novice political outsider Donald Trump became president of the United States. Trump’s success defied early predictions, with few political experts anticipating the intensity from his base of support when compared to more traditional candidates during both the Republican primaries and general election. Despite dispensing with traditional expectations and violating presidential debate norms, Trump’s performance and the associated audience response of raucous applause–cheering, laughter, and even booing during the initial 2016 primary debates (
Existing debate-focused research has documented the role of these salient media events in reinforcing existing preferences, producing issue knowledge, and influencing perceptions of candidate character, thus affecting undecided voter choices (
While providing useful insights concerning the impact of mediated events on electoral dynamics, these approaches do not take into consideration the unpredictable events that occur during debates and how they affect perceptions. Even after accounting for how campaigns pitch-and-spin their candidates’ performance (
Recent research addresses this oversight through continuous response measures (CRMs) and dial testing of debates, eye tracking of candidate exchanges, and focus group analysis of memorable debate moments (
Research considering OAR to political candidates at events such as debates tends not to focus on the audience itself, and its social influence on other audience members. The existing research that does consider OAR on participant evaluation, including those considering political figures, are experimental and do not disambiguate positive response such as laughter, applause, and/or visually oriented non-verbal signals (
While not dealing with political figures,
The existing research that does differentiate between the types of OAR tends to consider these utterances as a means by which large groups of followers provide feedback to their leaders. Specifically, audience responses such as applause–cheering, laughter, and booing provide audible signals indicating the type of response while indexing level of follower support or opposition. Furthermore, the timing of OAR indicates their level of synchrony with the speaker, as well as that with fellow audience members (
At the same time, media audiences, whether streaming the debates, watching on television, or listening through other broadcast media, as well as journalists reporting on the event, may be affected by this information. Indeed, OAR can lead to change regarding how the speaker is evaluated, indeed, even more so than the eliciting comments themselves (
Observable audience response such as applause–cheering, laughter, and booing may be seen as belonging to a class of behavior that is almost automatic and highly contagious, which in turn might lead to affective, cognitive, and behavioral response with political implications (
The overarching issue regarding OAR concerns their reliability in differentially reflecting the audience’s putative emotional and behavioral intent. Here, reliable indicators of emotion may be defined as being first, an accurate recognition of the emotional state of the communicator, and their resultant behavioral intent, and second, the signal being an index of the sender’s underlying state by being costly to produce (
Despite the rather sparse nature of existing research on location of debate viewing and audience composition, we expect differences in the in-person studio audiences and those having a mediated experience. In other words, the studio audience likely reacts differently from those watching a video of the event. This may be due in part to a location’s acoustic qualities that may enhance or diminish the subjective emotional and physiological response of audience members (
Thus, in addition to the type of OAR (e.g., applause–cheering, laughter, and booing) identified and potential mixtures that might occur, the intensity of studio audience response may be characterized by its length in time combined with its perceived audible strength. This intensity may in turn affect onlookers, whether in the studio audience – yet not affiliated with any social group or faction – or watching on television, live streaming over the internet, or listening on the radio and thus experiencing intra-audience mediated effects from the OAR (
Generally speaking, one can identify three general types of audible OAR as applause–cheering
Laughter is the most studied of all vocalizations discussed here; however, the focus tends not to be on the group. Individual laughter is focused on due to it serving as a pervasive social signal in interpersonal interactions by punctuating speech and indicating speaking turn taking and transition (
As a result, laughter may be seen as a costly signal by virtue of it either being evoked in a manner that is difficult to control whereas even emitted laughter that is initially faked leads to physiological change (
When considering group level behavior, research regarding laughter tends to focus on the target and intent of the verbal utterances leading to this type of response (
Of all the forms of OAR, applause–cheering is perhaps most likely to be observed in group settings such as political speeches and intra-party debates. This is likely due to the ease with which candidates are able to evoke it among supporters in partisan settings. As a result, applause–cheering has been appreciated for the role it plays in providing an important barometer of a politicians’ individual appeal during speeches (
On the other hand, due to applause–cheering likely not being as costly to produce physiologically and easier for audience members to inhibit than laughter (
Much rarer than supportive in-person audience response through laughter and applause–cheering at political events are boos and jeers (
In summary, laughter, applause–cheering, and booing provide means by which the audience physically present with a politician can communicate as a group in distinctive and easily identifiable ways. While pre-verbal, these OAR can successfully be used to strategically communicate factional preferences to not just the speaker, but also to other potential group members. As a result, there are social benefits and costs from participating or not participating in OAR; audience members must consider if engaging in different OAR types will be socially costly to them or if joining in with other audience members when candidates break norms of politeness and civility will pay off socially (
This report presents the findings of distinct, yet interconnected studies to explore the nature of OAR and their potential influence on evaluation of presidential candidates during a general election debate. We take a bottom-up/reverse engineering approach to study behavior as it occurs in a naturalistic environment (
We focus on the first general election debate between Donald Trump and Hillary Clinton in a multipart approach. Study 1 uses ANVIL content coding software to characterize and analyze studio audience response in terms of when the OAR occurred, what type they were (laughter, applause–cheering, booing, and mixtures), their duration and perceived strength, and in response to which candidate. Study 2 builds off of Study 1 by collecting and analyzing a unique dataset in which 362 undergraduate students took part in a field experiment watching or listening to the first presidential general election debate in seven different rooms. We use ethological analysis of the field study participants’ OAR by considering when different types occurred and how strong they were perceived to be by observers. This allows us to compare relatively unfettered field study audiences to the studio audience, where moderator instructions and politeness expectations presumably played a role in constraining an elite partisan audience, to the less inhibited university student-occupied rooms. We draw conclusions regarding both laughter and applause–cheering by considering four research questions concerning the co-occurrence of the OAR of laughter and applause–cheering (i.e., simultaneously occurring in both the studio audience and in the field study rooms). With Study 3, we evaluate the effect of studio audience laughter and applause–cheering on mediated viewer moment-to-moment (MTM) response of liking the speaking candidate. We finish this report by discussing the implications of our findings for future research.
The first of three general election debates between Democratic Party presidential nominee Hillary Clinton and Republican Party nominee Donald Trump occurred the evening of Monday September 26, 2016 and was hosted outside New York City by Hofstra University. Sponsored by the
Speaking time and studio audience OAR used ANVIL content analysis software, which allows for frame-by-frame coding (
Trump had nearly 5 min more speaking time at 47 min (2,795 s) when compared to Clinton’s 42 min (2,492 s). This was likely due to his interruptions, as Trump had nearly twice as many speaking turns (
A total of 34 OAR were identified during the debate proper (we did not code for the welcoming or concluding applause). These 34 studio audience OAR to the candidates’ statements/retorts – or in one case response to the moderator – lasted a total of 102.72 s and averaged just over 3 s (
In addition to evaluating length of the audience’s utterances, we coded for the subjective strength of these responses on a 1- to 5-point scale ranging from “barely audible” to “extremely audible” (
In comparison with previous general election debates (
The norms of civility respected in previous presidential debates by the audience through their OAR were not followed in the first 2016 general election presidential debate. Arguably, the norm-bending behavior of Trump through his many interruptions and perhaps more importantly, his use of laughter-inducing rhetoric led to the studio audience departing from customary expectations concerning their collective behavior (
Studio audience OAR to Hillary Clinton and Donald Trump during first 2016 presidential debate.
The ability of both candidates to instigate OAR suggests similarities; however, there are revealing differences. Specifically, while both Trump and Clinton invited equal numbers of studio audience applause–cheering with four apiece, Trump was able to elicit five more studio audience OAR than Clinton. This was mainly through his laughter-eliciting attacks; he was also arguably more polarizing by eliciting boos-jeering in one case and a combination of laughter and boos in another instance. For her part, Clinton produced laughter followed by cheers in two cases, suggesting unconstrained support by her followers, especially in response to her attacks on Trump.
Questions remain concerning the nature of the relationship between OAR by those in the studio audience and those watching the presidential debate on television, streaming on the internet, or listening on radio. Individuals hearing studio audience OAR in response to candidates utterances may potentially have also have experience intra-audience mediated effects through the OAR (
To understand the potential influence of both the candidate utterances and studio audience response on network viewers observing the televised debate, this study built from a field experiment being conducted at large university in the southern United States (
A total of 610 participants filled out an online omnibus survey prior to the debate (between August 29 and September 26, 2016) and were randomly assigned to one of seven rooms after their identity was verified. Each room, which was built to hold from 46 to 138 individuals, presented a different network (
The debate was viewed by 362 participants who took part as specified by university IRB protocols. Usable post-debate data from the 341 participants who filled out and returned the post-debate survey showed the sample was composed of 64% females, had a mean age of 19.53 (
Politically approximately 77% reported being registered voters, half (50.1%) self-identified as Republicans, just over a quarter (27.6%) as Democrats, and the remainder as independent/non-affiliated (22%). Political ideology as measured on a 7-point Likert-type scale (1 =
The participants were observed in seven different on-campus classrooms by study volunteers drawn from University Honors students and graduate students in Communication, Political Science, and Psychological Science programs. All rooms had three observers positioned at both front and one back room corners except the room watching ABC, which had six observers due to the additional observers mistakenly reporting to the incorrect room. Additionally, one observer was removed from analysis for coding only two OAR, when the average was 28.13 (
To analyze the co-occurrence of field study room OAR with studio audience OAR, in other words the intra audience media effects, data for each of the field study rooms were first considered in terms of what was being observed and measured before being aggregated for analysis. Thus, we initially consider how OAR is not necessarily experienced and coded in the same manner. First, an OAR may be experienced and coded as having greater strength due to the observers’ proximity to the individual(s)’ utterance, and not necessarily due to the entire room vocalizing at higher levels. Second, identification of OAR type, whether laughter, applause–cheering, booing, or combinations of these, may be influenced by the strength of the OAR itself. In either case, greater involvement from greater numbers of audience members might lead to either enhanced clarity of signal, or greater ambiguity.
Based upon the time of the occurrence and the comments, we were able to identify 113 unique OAR across the seven field study rooms with all showing a similar pattern (
Field study OAR to Hillary Clinton and Donald Trump during first 2016 presidential debate.
From this data, a clear pattern of agreement emerges: of the 321 verified field study room OAR correlating with candidate or moderator utterances, nearly four-fifths (
While it is apparent that laughter predominated and was the most easily identified of OAR, with from one-to-three coders (or in the case of the ABC room, one-to-six coders) in each room, the level of agreement does not necessarily reflect ICR so much as the location of the coder and the individual(s) audibly responding to the debate and the strength of the OAR itself. For instance, while when laughter occurred there was strong inter-observer agreement, the other types of OAR rarely resulted in agreement. There may be a notable relationship between the observers distinctively hearing laughter, applause, booing, or mixtures of these responses due to position in the room. Some coders may perceive one type of OAR as more prominent due to proximity to the audible response within the field study room. As such, inter-coder approaches typically used with content analysis (e.g., Cronbach’s alpha, Krippendorff’s alpha) are not appropriate; instead, we develop a variable of cumulative strength. Cumulative strength thus considers the OAR occurring in each room and creates an index where each of the observers, using the 1–5 strength scale used in Studies 1 and 2, add their scores together. Next, due to the disparity in the number of coders across all rooms, cumulative strength was standardized within each respective room by creating z-scores allowing us to compare across treatment rooms.
In keeping with the previous studies, and due to statistical reasons, we do not consider co-occurrence of studio audience and field study audiences deriving from studio audience applause-and-laughter (
When this categorical data is analyzed we find a highly significant relationship between types of studio audience and field study OAR co-occurring, χ2(2,306) = 27.790,
To assess the effect of the studio audience OAR on cumulative strength of field study OAR, we ran 3 (type of studio audience OAR: laughter, other, no response) × 7 (field study room) ANOVA on cumulative strength of OAR in field study rooms. Findings suggest the difference in the type of OAR was highly significant and had a strong effect [
Finally, to assess the influence of the intensity of the studio audience OAR on field study room OAR we considered only those cases in which there was a co-occurrence of studio audience and field study OAR. This leaves us with a truncated sample of 131 events. To consider the effect of the studio audience OAR type and intensity on the field study’s OAR type and the standardized cumulative strength, we carried out a binary logistic regression and an ANCOVA, respectively. Both equations include the studio audience OAR intensity index as a covariate with the type of studio audience OAR (laughter or other) as a between-subjects factor.
The binary logistic regression analysis considered the field study audience rooms laughter or other OAR type was predicted by studio audience laughter or applause and the intensity of their response. The full model was significant χ2(1) = 20.495,
Analysis of the effect of studio audience OAR type and intensity on field study room OAR standardized cumulative strength, on the other hand, suggest both variables have influence. Findings show the studio audience OAR intensity index was significant, had a small effect, and was positively related to field study OAR (
Despite taking a conservative approach regarding our analysis of co-occurring studio and field study OAR by not including those studio audience events where applause–cheering followed and combined with laughter, our findings indicate laughter was more evident in the field study rooms than in the studio audience. When co-occurring with studio audience OAR, there was a moderately strong relationship between the type of studio audience OAR (laughter or applause)/non-response and the field study audiences OAR type, with applause–cheering significantly less likely to co-occur with laughter.
Furthermore, the more stereotypical signaling nature of laughter, when compared with other types of OAR, is apparent even when taking into account the “success” of candidate utterances (as indexed through studio audience audible intensity). This may be seen as indicating laughter, even when aggregated in OAR, being more automatic and stereotyped when compared with all other responses, even when considering observational judgments.
While the findings are illuminating, it should be noted that younger audiences such as studied here will likely laugh more due to social pressures, such as the implicit lack of knowledge concerning the status/rank of those around them (
Participants were recruited from a west Texas community as part of an election study announced on the local newspaper’s website. Due to continuous response theater using dedicated wireless dials, sample size was limited to 34 participants—the maximum number the room could accommodate during the debate. Partisan identification was divided between 14 Republican Party identifiers, 11 Independents, and 9 Democratic Party identifiers. Participants received a small monetary inducement in exchange for their participation. Age ranged from 18 to 73 (
The dependent variable,
To calculate participant response to studio audience laughter and applause–cheering during the debate, the MTM responses 10 s prior to the onset of studio audience OAR provided a baseline average from which deviations up to 5 s afterward were considered. Thus, positive MTM change scores represent a more favorable attitude toward the candidate. The first 5 s after the onset of OAR was analyzed in order to account for potential delayed MTM reaction to OAR, as well as the average duration of OAR lasting roughly 2–3 s.
Nineteen studio audience OAR comprised of laughter and 11 of applause–cheering identified in Study 1 are considered, with overlapping or indistinct OAR removed from analysis. Of these, nine studio audience laughter segments and five applause–cheering OAR occurred during or after Hillary Clinton’s comments, while 10 laughter and 6 applause–cheering OAR occurred during or after Donald Trump’s comments.
To address the research questions, an omnibus 2 (studio audience OAR: Laughter v. Applause) × 3 (partisan affiliation: Democratic v. Republican v. Independent) × 5 (Time) repeated-measures ANOVA was conducted. Because all participants evaluated every studio audience OAR, studio audience OAR and time (i.e., change scores for the 5 s after onset of laughter or applause) served as the within-subjects repeated measure. Political affiliation served as the sole between-subjects variable.
The main effect of studio audience OAR on MTM response in the continuous response theater was not significance [
Main effect of studio audience OAR on moment-to-moment response 1–5 seconds after onset.
The main effect of political affiliation on MTM responses was significant [
Partisan moment-to-moment response to applause-cheering for Clinton 1–5 seconds after onset.
After studio audience laughter, participant MTM response didn’t significantly differ between the three political affiliations [
Our findings suggest that studio audience applause–cheering had an effect on continuous response study participant candidate evaluations, whereas laughter did not, and that political party affiliation further clarified differences in how likeable the candidates were perceived; however, these findings might not adequately reflect the influence of OAR type. First, this study’s sample was quite small at less than one-third of the comparable studies by
While not as tidy as laboratory experiments, we believe that the enhanced generalizability of our analyses of multiple studies by using ethological methods most proximately building on those pioneered by Robert Provine in his research on laughter (
Observable audience responses such as laughter, applause–cheering, and booing are important because they reflect the emergent properties of individuals becoming groups. While the research reported here does not purport to explain OAR or appraise intent, it makes an important first step in providing evidence concerning individual humans engaging in the group behaviors of applause–cheering, laughter, and (to an extent) booing. In addition to serving the more theoretical purposes of understanding social identity with its evolutionary roots of followership and in-group vs. out-group identities (
Findings regarding the specific research questions posited and evaluated in Study 2 suggests that there is a moderately strong relationship between not just the studio audience and the field study audience OAR, answering Research Question 1, but also between laughter occurring in the studio audience and in the field study rooms. When the truncated model was considered, allowing for us to control for studio audience OAR intensity, we found laughter in the studio audience was more strongly related with field study room laughter than applause was with the “all other types” category we used for the field study rooms. This provides evidence responding to Research Question 3. However, while there was modest evidence for Research Question 2, as studio audience OAR intensity was weakly related with field study room cumulative OAR strength, we find, regarding Research Question 4, that there is not a significant relationship between studio audience intensity and OAR type.
The differential response to studio audience OAR was further probed by continuous response measurement (CRM) of MTM liking of the speaking candidate. This allows us to move beyond our research questions to more directly draw inferences. The greater amounts of studio audience laughter elicited by Trump in comparison with Clinton may have affected unaffiliated viewer perceptions by evoking the behavioral mimicry that presumably occurs before social contagion. However, the applause–cheering evoked by Trump may have mattered more, as well as the intensity of the evoked studio audience OAR. Specifically, it appears that the likability of Trump was positively affected by audience applause–cheering to a significantly greater extent than laughter with the CRM study, and that the applause–cheering for Trump was more effective than that elicited by Clinton. In combination with the observational studies regarding the field study, the lack of studio audience control by the moderator may have affected viewer perceptions not just through the stereotypical laughter that is mimicked near automatically, but also by the applause–cheering and mixed audience responses that increase their likability to partisans.
While the information found through the three studies regarding the first general election debate of 2016 helps clarify the role group response in the form of OAR plays, a series of broader questions remain. Specifically, it has been established that individual laughter is a “costly signal” involving abrupt eruptions of distinctive vocalizations concomitant with physiological and emotional change (
Likewise, questions still remain regarding how individual responses aggregate into a group response. In other words, applause–cheering, laughter, and booing apparently are mimicked, albeit at different levels based upon the audience, and may potentially be socially contagious. As seen in this study, the shared, and potentially mimicked and contagious experience of co-occurring OAR between the studio audience and the field study rooms raises questions. The first, and perhaps foremost, concerns which form of OAR is more likely to lead to group coordination in the form of greater support for goals as stated by the speaker, as well as support for the leader herself or himself. Specifically, while laughter appears to be more likely to be shared than applause–cheering, the nature of booing is not as well established due in great part to its rarity.
At the very least, Studies 1 and 2 suggest a high level of mimicry by individuals, especially regarding laughter. Here, mimicry is defined as the quick and spontaneous matching (within 1 s) of another person’s display behavior and linked with empathy and prosocial behavior (
A further question concerns whether there are optimal audience sizes for these different forms of OAR; in other words, there tends to be a greater likelihood of applause–cheering, laughter, and booing based upon the increasing size of a group in a form of mutual “grooming” (
Future research thus should be able to better disambiguate not only the audible signal of group response, but also understand attitudinal and behavioral change. Advances in technology should allow for more precise measurement than that carried out here by naïve judges with limited training. Specifically, audio recorders (including smart phones) placed throughout the room might allow for more accurate notation of OAR timing, type, and intensity, even to the individual level. Indeed, as seen with acoustic research regarding laughter, the different utterances might have a range of signal qualities that are not being considered in needed detail. Just as laughter itself may embody many different emotional messages by reflecting the responses of many different individuals, the resulting message may “get lost in the crowd.” Therefore, by understanding more perfectly the union in OAR such as laughter, applause–cheering, booing, and their combinations, we may be able to divine a greater understanding of the most fundamental of human social activities – politics.
Previously presented at the 75th Annual Midwest Political Science Association Conference, April 5–9, 2017, Chicago, IL, United States.
This study (IRB Protocol #: 16-07-029: “The 2016 Presidential Election: Attitudinal Change in Response to Campaign Events, Debates, and Electoral Results”) was carried out in accordance with United States Federal Regulations concerning research [45 CFR 46.102(d)] and human subjects [45 CFR 46.102(f)] as implemented by the University of Arkansas, Fayetteville Office of Research Compliance Institutional Review Board. All participants were given written informed consent in accordance with these United States Federal Regulations and the Declaration of Helsinki.
PS: data collection, data cleaning, theory building, writing, and data analysis. AE: data collection, data cleaning, data analysis, and figures. RD: data collection, data cleaning, and editing. ZG: data collection, data cleaning, data analysis, writing, and figures. EB: data collection, data analysis, writing, and editing. RW: data collection and editing. SE: data collection.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
These two forms of audience audible utterances are combined for the sake of this analysis; we do appreciate that they reflect different kinds of communication using different non-verbal channels (manipulation of hands and vocalizations) (