Australasian Marketing Journal 21 (2013) 205–211

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Australasian Marketing Journal

journal homepage: www.elsevier .com/locate /amj

The emotions that drive viral video

1441-3582/$ - see front matter � 2013 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.ausmj.2013.07.003

⇑ Corresponding author. Tel.: +61 8 8302 0825.E-mail address: Karen.nelson-field@unisa.edu.au (K. Nelson-Field).

Karen Nelson-Field ⇑, Erica Riebe, Kellie NewsteadUniversity of South Australia, Ehrenberg-Bass Institute, 70 North Terrace, Adelaide, SA 5000, Australia

a r t i c l e i n f o a b s t r a c t

Article history:Received 19 April 2013Revised 26 July 2013Accepted 29 July 2013Available online 27 August 2013

Keywords:Viral marketingVideo sharingSocial media

In today’s socially connected world marketers are turning to social video as a way of extending campaignreach and gaining cut-through. However knowledge on which creative characteristics are related to suc-cessful diffusion, is limited. In this research we consider how two constructs of emotional response (arou-sal and valence), both separately and collectively are related to how videos are shared. Two large datasets are considered, one commercial and one non-commercial (n800), with levels of actual daily sharingrecorded for all videos examined. We find that high arousal emotions are the primary driver of videosharing and while valance plays a role, it does so to a lesser extent. This study is the largest of its kindand makes a significant contribution to our understanding of what makes a successful viral video.� 2013 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.

1. Introduction

Content sharing is the fastest growing activity amongst Face-book users, with 7 billion pieces of content shared each week (Un-ruly Media 2012). This is in part the result of the unrivalled growthof social media in recent years, and in part due to the emergence ofthe ‘Connection generation’ that is said to crave interaction with,and connection to, vast networks like never before (Pintado,2009). For marketers this provides an unparalleled opportunityto disseminate commercial messages to literally hundreds of thou-sands of potential brand buyers in a matter of days, without thecost associated with traditional mass media. Increasing clutterand audience fragmentation, however, offset such opportunityand pose challenge to those attempting to achieve message cut-through in an efficient manner. Thus marketers are increasinglyturning to the diffusion of video as a way to gain cut-throughand reach in the social media space (comScore, 2011; Greenberg,2010; Madden, 2009; Purcell, 2010; Southgate et al., 2010).

The rise of video sharing giant YouTube combined with im-proved sharing functionality across most social networking sites,has cemented the role of viral video in the marketing mix of manycorporates (Cashmore, 2009; Eckler and Bolls, 2011; O’Malley,2011; Tsai, 2009). When executed correctly, a viral video campaignis said to offer the marketer benefits such as extended campaignreach, reduced advertising avoidance and earned publicity for thebrand (Dobele et al., 2007, 2005; Eckler and Bolls, 2011; Hannet al., 2008; Southgate et al., 2010).

While the potential advantages of viral video would seemappealing for marketers, success can be hit and miss. Some videos

are shared tens of thousands of times in a few short hours, whileothers fall very short of expectations. Why certain pieces of videocontent get shared more than others is largely unknown. This re-search aims to shed some light on a small piece of a big puzzle.

2. Literature review

Given the increasing investment in social video campaigns, rel-atively little is known about the elements of a successful campaign.Extant research falls broadly into 4 areas:

1. How viral content spreads between individuals, which oftendraws on epidemiology insights regarding social contagion(van der Lans et al., 2010);

2. How to effectively manage a campaign by optimising target-ing and seeding efforts (Bampo et al., 2008; Hinz et al., 2011);

3. The behavioral characteristics (i.e. motivation) of receiversand senders of viral content and the impact of incentives thatare provided to encourage social sharing (Arndt, 1967; Riméet al., 1998); and

4. The creative attributes of successful viral content.

This final area is where our research makes the greatest contri-bution. We acknowledge that other factors likely play a role in thesuccess of a viral campaign (for example consumer attitudes andmotivations, the characteristics of the product itself, the function-ality of the platform and optimisation strategies (Berger and Sch-wartz, 2011; Moldovan et al., 2011; Phelps et al., 2004; Woerndlet al., 2008). However this study focuses on the emotionalresponses that video content elicits and the propensity of the audi-ence to share those videos given the emotions evoked. We draw on

206 K. Nelson-Field et al. / Australasian Marketing Journal 21 (2013) 205–211

a range of studies that have, to varying degrees, considered the roleemotions play in content sharing, however, our study investigatesthe most extensive array of potential emotional responses to date.Similarly, we consider the role these emotions play using actualsharing data in a social media context. Previous research has in-stead looked at intent to share, or other media formats where shar-ing is a very different behaviour.

2.1. Creative characteristics

Perhaps the earliest empirical research on the creative charac-teristics of ‘successful’ viral material was undertaken by Porterand Golan (2006). The authors conducted a content analysis of tele-vision and online commercials to determine whether the materialfound on traditional advertising differed to that within viral adver-tisements online. They found that differences do occur between thetype on material applied in a TV campaign as compared with on-line; that online material commonly contained provocative compo-nents (such as sex, nudity, and violence). But this is hardlysurprising due to the restrictions around content on commercialtelevision. The TV sample was collected randomly while the onlinesample was collected from a pool of award winners in the viraladvertising field (Future Marketing Awards). From this the authorssuggest that successful online videos contain provocative creativeelements and infer that such devices appear to motivate consumersto pass along content online. A noted limitation in this early studyexplains that using award winning viral videos may skew they re-sults, and furthermore, it was not possible to determine the actualpass-along rate for video content at the time of the study.

Brown et al. (2010) also considered provocative advertising ap-peals, specifically, comedic violence. However unlike Porter andGolan (2006), Brown et al. (2010) suggested that the strength ofan audience’s emotional reaction to the campaign would also affectwhether that content would be shared. They drew this conclusionbased on their finding that humorous material, when it embodiesviolence and consequence severity, is most likely to be shared. LikePorter and Golan (2006), Brown et al. (2010), used a sub-optimalmeasure of real sharing behaviour by considering respondentpass-along probability in modified Juster scale. They also investi-gated just a single, well-known brand (Coke), which may have ren-dered bias for the results of the study due to the sample’sfamiliarity with the brand (Elberse et al., 2011; Maclnnis andJaworski, 1989; Peracchio and Meyers-Levy, 1997).

To our knowledge Southgate et al. (2010) presents the onlystudy that has been conducted directly in the social media context.Their aim, to consider what makes an advertisement so good thatviewers are driven to share it, promote it, discuss it or search for it.

The authors are critical of the Porter and Golan study (2006) bysuggesting that the word provocative is unnecessarily prescriptiveand while some of the viral ads coded in their study may be pro-vocative, there are clearly others, (they use the example of T-Mo-bile’s ‘Dance’), which are not. So unless 100% of the online datarendered a provocative result (all of which won viral awards), theremay be more to the sharing story. While we agree with Southgateet al. (2010) their own sample is restricted to commercials madeavailable through the Millward Brown proprietary testing tool(Millward Brown’s Link™). In their own admission this data isskewed to those that render an above average result in terms ofthe ‘involvement’ metrics embedded in the tool. The proprietarypre-testing tool comprises around 150 25-min interviews abouteach of the advertisements. Merging this data with the weeklyYouTube viewing data the authors conclude that elements ofinvolvement and enjoyment are the main mechanisms for onlineviewing of video content. Due to the proprietary nature of the toolno more detail on what constitutes involvement and enjoymentcan be found, however they conclude their paper by suggesting

that creative elements in an advertisement account for over halfof the variation in viral performance.

Like other studies, however, they restricted their research tocommercial content that was also available on television. Thereare many underlying implications here. Online-only campaignsare not subject to the same legal restrictions by which TV cam-paigns need to abide; hence the nature of the creative is not repre-sentative of what is available on YouTube. Arguably, the televisionscreening of this material may have had an impact on online view-ing, therefore the degree to which it is viewed is more an outcomeof its TV exposure. Which brings us to criticism of the use of views.The use of viewing (rather than sharing) may present a limitation,as views are likely driven by a number of external factors includingother advertising activity. We would suggest that actual shares arethe best measure of forwarding behaviour.

2.2. Emotional appeals

Dobele et al. (2007), in their investigation of viral campaigns,argued that it is emotions experienced by the viewer (and thestrength with which they are felt) not creative elements that areused within the content that trigger forwarding behaviour. Dobeleet al. (2007) examined six primary emotions (surprise, fear, sad-ness, joy, disgust and anger), with sub-emotions used to capturethe total strength with which primary emotions were felt. Forexample, discouraged, mad and enraged were the sub-emotionsthat were used to describe the total strength with which the pri-mary emotion of anger was felt. Similarly, amazement and aston-ishment were classified as sub-emotions of surprise. Theyconsidered nine commercial viral campaigns, which varied in theiruse of delivery mechanism (i.e. eNewsletter, video, petition, etc.).They concluded that an emotional response to the viral campaignalone was not sufficient to trigger sharing and that content that in-cludes an element of surprise is key to diffusion. The authors sug-gest this can be generated through strongly felt feelings such asamazement and astonishment.

The sample used in this research however was again restrictedto ‘successful’ campaigns potentially rendering bias to the results.Furthermore ‘success’ is not simply related to the degree to whichthe message spread, rather success includes campaigns that in-creased turnover, sales or brand development. With such con-founding factors in the dependent variable it would be hard forthe authors to disentangle any real results.

Nonetheless, Poels and Dewitte (2006), Binet and Field (2007)and Rimé et al. (1992) all draw this same conclusion as Dobeleet al. (2007) when discussing the phenomenon of social sharingin the psychology literatures. They concluded that most emotionalexperiences are shared shortly after they occur and that the extentof sharing (in relation to the frequency and number of people withwhom the incident was discussed) is directly related to thestrength of the emotion felt. Further Bell and Sternberg (2001) con-sidered how emotional strength affects the pass-along rate of‘memes’ (rumor, folklore, urban legend, chain letters). They studied112 memes and found that stories are ‘passed along’ when theyevoke stronger emotions, such as disgust.

2.3. Emotions and valence

While the research discussed to this point suggests that the nat-ure and strength of the emotional response will affect the likeli-hood of content being shared with others, it does little toadvance our knowledge of whether positive or negative emotionalresponses are preferable.

Eckler and Bolls (2011) extended the work of Dobele et al. (2007)to explore how emotional tone could drive content sharing. Theyconceptualised emotional tone as a feature of valence i.e. whether

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the emotion felt was pleasant, unpleasant, or coactive. Respondentsviewed 12 commercial videos and reported their intent to forward.They reported that the emotional tone of content affects forwardingintention, and in particular that positive tone has the greatest influ-ence. Interestingly, however, with the exception of the Eckler andBolls (2011) study, research on the sharing of social experiences(rather than video content) instead concludes that valence doesnot play a role. This is presumably the rationale of content produc-ers who believe that campaigns that elicit negative emotional re-sponses, such as fear, anger or sadness will be equally successfulin generating sharing as those campaigns that elicit positive emo-tional responses such as happiness or love (Rimé et al., 2011).

2.4. Emotions, valence and arousal

In the largest study to date of the emotions and content sharing,Berger and Milkman (2012) considered how emotional responsewas linked to the email sharing of articles from the New YorkTimes. Both positive and negative emotions were investigatedbut the authors further tested emotional strength taking a physio-logical approach to measurement. The authors suggest that ‘arou-sal’, an established construct of emotion (Baumeister andBushman, 2010), might be the key to driving viral diffusion. Forexample, while anger and sadness are both negative emotions,the degree of sensory alertness (or arousal) associated with themdiffers (e.g. feeling anger may increase the heart rate, while sad-ness may not). The authors hypothesized that high arousal contentwill be positively linked to virality, while low arousal content willbe negatively linked to virality. They found that content that in-cited more arousal (either positive or negative) was shared morethan those that incited less arousal, and that articles that were ofa positive nature were shared more than negative ones. While thisstudy considered actual information sharing (i.e. whether the arti-cle made the New York Times’ ‘most emailed’ list or not), thedependent variable was dichotomous. How much more sharedone article was over another relative to the emotions evoked wastherefore not considered. Additionally, by comparison to previousstudies, this research simply used 4 emotions; two that repre-sented high arousal negative emotions (anxiety and anger), onethat represented a high arousal positive emotion (awe) and onethat represented a low arousal negative emotion (sadness). Thisis a potential limitation in that the imbalance of positive and neg-ative emotions may affect the results, however the authors justifythe imbalance suggesting that it is easier to differentiate and clas-sify negative emotions than positive ones. An additional limitationof this study is the nature of the sample, only content that hadbeen accessible by New York Times subscribers was considered.

Table 1Summary of prior knowledge.

Study Type stimuli nStimuli

Independent variable

Porter and Golan(2006)

TV and onlinecommercials

501 6 Advertising appeals

Dobele et al. (2007) Various viral campaigns 9 6 Primary emotions

Brown et al. (2010) Purpose designedcommercials

4 Comedic violence inteconsequences

Southgate et al. (2010) TV Commercials onYouTube

102 Creative drivers &distinctiveness

Eckler and Bolls (2011) Online commercials 12 ValenceBerger (2011) Films 1 Arousal indexBerger and Milkman

(2012)NY Times online articles 7000 4 Emotions (valence &

Berger also tested the theory that arousal increases social trans-mission in two smaller experiments (Berger, 2011). One experi-ment exposed respondents to a high arousal film treatment totest ‘willingness to share’ after exposure. The second experimentconsidered arousal outside of the context of emotional content.Respondents were induced into a state of arousal via physical exer-cise then their email sharing behaviour was monitored. Bothexperiments rendered the same results as the New York Times re-search, concluding that arousal is key to the diffusion of content.

Notwithstanding the importance of literature related to epide-miology, optimisation and motivation, the following table summa-rises the past studies in relation to the creative attributes ofsuccessful viral content (see Table 1).

3. Hypotheses

Based on the extant literature specific to content forwardingand creative characteristics, we explore the following hypotheses

H1. Videos that evoke high arousal emotions (either of positive ornegative valence) will be shared more than those that evoke lowarousal emotions (either of positive or negative valence).

H2. Videos that evoke high arousal positive emotions will beshared more than those that evoke equally high arousal but nega-tive emotions.

H3. Videos that evoke positive emotions will be shared more thanthose that evoke negative emotions (regardless of the level of arou-sal that is elicited).

While the hypotheses are directed by the findings of Berger andMilkman (2012) we extend this, and other research, by investigat-ing content sharing via a social networking platform (Facebook).Sharing material in this way may be expected to produce differentkinds of sharing behavior than email (Berger and Milkman, 2012)given the different levels of risk the sharing platforms create forthe sharer. For example, one’s entire social network is exposed tothe sharing of material on Facebook, typically with a single behav-iour. In comparison, sharing via email is typically more involved(i.e. the sharer chooses to share with each member of their net-work individually) and therefore is more considered. We mighttherefore expect higher levels of sharing in a social media setting,and greater potential for variance between videos that elicit differ-ent emotional responses. Furthermore, in comparison to manyother studies in this area, we look at the influence of emotional re-sponse on actual sharing levels, rather than predicted or intended

Dependent variable Linked to virality

Provocative material present/notpresent

Sex, nudity, violence

Success based on measures of ROI anddiffusion

Strongly felt emotions

nsity/ Pass-along probability Humour high in violence/severity

YouTube views Involvement andenjoyment

Pass-along probability Positive tonePass-along probability High arousal

arousal) Emailed/not High arousal

Table 2Valence and arousal grid.

Emotionaldescriptor

Positive Negative

High arousal Low arousal Higharousal

Lowarousal

Humour Hilarity Amusement Disgust DiscomfortMotivation Inspiration Calmness Sadness BoredomTemperament Astonishment Surprise Shock IrritationAwe Exhilaration Happiness Anger Frustration

1 Cambridge Advanced Learner’s Dictionary, Cambridge University Press, 2011.

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pass-along. Even in comparison to the Berger studies, we makesome improvements to the dependant variable used, by lookingat volume of shares. Also in comparison to other research in thisfield we consider non-commercial data. While the commercialimplications of this research will be of most interest to readers,the inclusion of non-commercial data provides a useful bench-mark. Doing so controls for any influence that commercial adver-tising may have had on our results had the study been limited tojust commercial video content. For example brand size effectsand seeding and targeting efforts.

There is still much to learn about how an audience’s emotionalresponse to viral video content directly affects whether, and towhat degree a video will be shared. The following list summarisesthe intended contribution of this study:

(a) Two large data sets are used. One of non-commercial con-tent (n400) and one of branded content (n400). Looking forresults over a range of conditions adds greatly to the gener-alisabiltiy of the results.

(b) The dependent variable in this research is actual sharingdata, not coder predictions, views or any other intermediaryvariable of audience behaviour;

(c) This research moves past the simple definition of emotivebased on message content and moves toward the use ofaudience response.

(d) Our study involves multiple raters coding 100% of the videosto ensure coder differences are not the cause of resultsachieved;

(e) Emotion intensity is measured by arousal pairs not scalesthat are more subjective;

(f) Much of the data (at least half) is content designed foronline, not television content placed online;

(g) This research considers high sharing and low sharing videos,rather than a skew towards ‘successful’ stimuli;

(h) Finally, of most interest to marketing practitioners, the dataalso captures the extent of sharing and its relationship withemotions (i.e. we use a continuous not dichotomous sharingvariable).

4. Research design and method

4.1. Data

We begin with 400 non-commercial (user-generated) videosand then extend our analysis to a further 400 commercial videos.

The 400 non-commercial videos, along with sharing informa-tion for each video, were collected from a freely accessible aggrega-tor site (Facediggs). This included the number of shares viaFacebook per day for each video since its launch date (allowingfor the valid comparison of videos that had been recently launchedwith those that had been available for many months). The datawere collected in early 2011. The level of sharing in the data ran-ged from 1 share per day to just over 109,000 shares per day.

The 400 commercial videos used in this study were selectedrandomly from UK based agency Unruly Media who catalogue aglobal database of all videos shared since 2006. The videos adver-tised a wide range of products and services (e.g. motor vehicles,technology, fast moving consumer goods, insurance, finance,etc.). Sharing information for each video was also collected. WhileUnruly’s database includes share information from Facebook, Twit-ter and Blogs, to keep the second set of data consistent with thefirst, only shares via the Facebook platform were analyzed. Morethan 95% of all shares occur via the Facebook platform, and so ana-lyzing only Facebook shares is not considered a limitation of thisresearch. The level of sharing in this data ranged from 3 shares

per day to just over 52,000 shares per day. These data were col-lected in late 2011/early 2012.

4.2. Method

The primary aim of this research was to understand how shar-ing is affected by the extent to which video content elicits particu-lar emotional responses from its audience. It was thereforenecessary to determine the emotional response that a viewermay have to all 800 of the videos included in the study. This wasestablished using a total of 28 coders. Coders were asked to watcha video in its entirety then to tick and emotional response from alist of 16 potential emotions (see Table 2), that best describedthe emotion educed whilst viewing To reduce fatigue each coderwas provided with 50 videos of the total 800. To reduce confusionof terms coders were provided a dictionary definition of the 16emotions and sat through a briefing session on the meanings ofeach emotion, and provided some example videos to code. How-ever at no point were coders alerted to the hypotheses of the study.

To minimise subjectivity, often analogous with emotions coding(Heilman, 1997) each video was coded by two individuals (i.e. over-all 800 surveys were collected for each set of 400 videos). There wasa high level of inter-coder agreement across both sets of data (avg.92%) suggesting that a wider audience would have a similar reactionto the videos included in the study. In addition the use of a largesample of videos helps to minimise any potential impact that anyone coder may have had on the results and implications of this re-search. Each coder was required to watch each video in its entiretyand then use online survey software Qualtrics to enter their coderID, the video number and their single emotional response. Emotionswere listed alphabetically for ease of identification for coders.

The list of potential emotions was developed based on the de-sire to capture both low and high arousal, both positive and nega-tive responses. Each emotion was classified as high or low arousingbased on previous classifications in the literature. While manywould agree on what would constitute a negative or positive emo-tion, the level of arousal that is associated with particular emotionsis notably more subjective (Heilman, 1997). This is evident in theliterature, as the classification of high and low arousal emotionsdiffers amongst authors. For example, Berger and Milkman classifysadness as a low arousal emotion, while Cacioppo et al. (2000) clas-sify it as a high arousal emotion. Similarly, Berger and Milkmanclassify joy as a low arousal emotion, while Dobele et al. (2007)classify it as a high arousal emotion. To limit subjectivity emotionswere grouped as pairs. Pairs are useful when the underlying scaleof measurement is subjective (Thurstone, 1927). Each groupingrepresents the most diametrically opposed emotions of the pri-mary descriptor being measured. For example, the definition ofsurprise is ‘the feeling caused by something unexpected’.1 The def-inition of astonishment is ‘extreme surprise’,1 making it an appropri-ate high arousal positive emotional term (and a suitable pair forsurprise) for the primary descriptor of temperament. Table 2 showsthe full list of 16 emotions (and the 4 descriptors they capture) and

Table 3Prevalence of emotions elicited by video content (%).

Emotional Descriptor High arousal positive Low arousal positive High arousal negative Low arousal negative Totals

N-C C N-C C N-C C N-C C N-C C

Humour Hilarity 9 10 Amuse. 29 28 Disg. 2 2 Discom. 3 2 43 42Motivation Insp. 4 5 Calm. 5 7 Sad. 2 2 Bore. 26 21 37 35Temperament Aston. 4 4 Surp. 3 4 Shock 1 1 Irrit. 1 2 9 11Awe Exhil. 1 2 Happ. 8 9 Anger 1 0 Frust. 1 1 11 12

18 21 45 48 6 5 31 26 100 100

K. Nelson-Field et al. / Australasian Marketing Journal 21 (2013) 205–211 209

their classification as either positive or negative valence and high orlow arousal level. The specific emotions used in the study weredrawn largely from the psychology and neurophysiology literature(Baumeister and Bushman, 2010; Griskevicius et al., 2012; Riméet al., 1998; Turner, 2007).

Perhaps important to note here, while rating scales have beenused to measure emotional intensity in other studies of viral mar-keting, using variants of primary emotions to classify intensity isconsistent with other research on emotional responses (Cacioppoet al., 2000; Turner, 2007). There are, therefore, some differencesbetween our classification of emotions and that of others. For in-stance, where Berger and Milkman (2012) reported high and lowlevels of amusement, we chose instead to use hilarity as the higharousal variant of the primary emotion of humor and amusementas the low arousal variant. Negative valence emotional descriptorsof this primary emotion were disgust (for high arousal) and dis-comfort (for low arousal). Given that coders had been providedwith the relative valence and arousal of each emotion, we didnot ask coders to indicate strength of an emotion on a rating scale.

Having gathered the emotional reactions that each of the videoselicited from their viewers, the average number of shares per dayfor videos that elicited one form of emotional response was com-pared to that of videos that elicited a different emotional responsefrom their audiences.

5. Results

5.1. Prevalence

We begin by reporting the prevalence of videos that draw par-ticular emotional responses from their audiences. Inevitably, someemotional responses will be more rarely elicited by video contentthan others. This is an important consideration in relation to inter-preting how emotional responses are related to the sharing ofthese videos. For instance, if there are only a small number of vid-eos that elicit sadness, comparison of average sharing levels withanother rare form of video will need to account for large error mar-gins around these two averages. In comparison, comparing theaverage number of shares for videos that elicit more common emo-tional responses will be subject to far less error. In addition toreporting the proportion of videos that elicit each form of emo-tional response, we therefore also report a ratio of prevalence tosharing when comparing average levels of sharing. This allows usto control for potential differences in the error margins for moreand less common forms of video and provides a more accuratecomparison of sharing levels for videos that elicit different emo-tional responses from their audiences. Rather than consideringthe impact on sharing of each separate emotional response, we alsofocus on comparison at an aggregate level when interpreting thesharing data. That is, we report how videos that induce any higharousal emotion differ from those that induce any low arousalemotion, and how those that elicit any positive emotional responsediffer from those that elicit any negative emotional response. Thuswe describe findings for each of 4 arousal/valence emotion groups;high arousal positive; low arousal positive; high arousal negative

and low arousal negative. This maximises the number of videosthat are used to calculate the average level of shares per day usedfor comparison, and so further reduces the error that would beassociated with comparing shares of videos that elicit each sepa-rate emotional response.

When making the comparison of average sharing per day forvideos that elicit different emotional responses we use one-wayand two-way (4 � 2) ANOVAs. The existence of a high level of mul-ti-collinearity between individual emotions (which were, by de-sign, opposing pairs) means that this is the most appropriatemethodological technique for testing the hypotheses.

Table 3 shows the proportion of all videos examined that elic-ited each emotional response. There was wide variation in the ex-tent to which videos elicited different emotions, although elicitinghumour and motivation (mostly in a low arousal form of amuse-ment and boredom) was the most common response for both thenon-commercial (N-C) and the commercial (C) videos. It is notablethat there is very little difference in the emotions that are elicitedby either commercial or non-commercial content.

Finding 1 – Videos that elicit a low arousal response are morecommon.Finding 2 – Videos of positive valence are more common.

It is perhaps unsurprising that video content that elicits a lowarousal emotional response is more common than that whichmoves us more powerfully. Three quarters of all video materialelicited low arousal emotions. This suggests that it is easier to de-velop material that is lacking in emotional strength than it is to de-velop material that is emotionally evocative. Equally, it is alsounsurprising that positive content is more often produced thannegative content. Even when the impact of boredom is taken intoaccount, videos that stimulate a positive emotional response werestill more common than negative ones. This is particularly so forcommercial videos, few of which elicited boredom and none ofwhich incited anger. However the real question lay in whetherthese forms of content are more effective in inducing sharingbehavior.

5.2. Arousal and valence

We now describe the average number of shares per day of vid-eos that elicited emotions in each arousal/valence group (Table 4)across both sets of data. Videos that elicit high arousal emotionalresponses (either in their positive or negative form) attract moreshares than videos that elicit low arousal emotional responses(around twice as many). This pattern holds across both the com-mercial and non-commercial data. This variance in shares perday between arousal groups was also found to be significant(p < .001). The table further shows that videos that elicit positiveemotional responses are shared more than those that elicit nega-tive (but equally arousing) emotional responses (also significantat the p < .01 level). In addition, non-commercial videos elicit moreshares than commercial videos (p < .01). These findings are consis-tent with the hypotheses.

Table 4Ratios of shares to prevalence per arousal group.

Valence Positive Negative

Arousal High Low High Low

Data Set N-C C N-C C N-C C N-C C

n 152 177 357 384 46 36 245 202Total avg. shares per day 1,002,567 51,7410 1,012,758 671,025 228,976 77,455 235,214 368,991Prevalence: shares 1:6596*+ 1:2923** 1:2837+ 1:1747 1:4987* 1:2152** 1:960 1:1645

* p < .001.** p < .01.

+ p < .01.

210 K. Nelson-Field et al. / Australasian Marketing Journal 21 (2013) 205–211

This data alone would suggest support for each of the hypothe-ses. However, as acknowledged above, it is important to accountfor the prevalence of videos that are able to elicit each form ofemotion in order to produce a result that is statistically reliable.To do this, we generated a ratio of prevalence to average per daysharing, which is also included Table 4. This shows the averagenumber of shares per day for each video that elicits a particularemotional response. For instance, every non-commercial video thatelicits a high arousal positive emotional response was shared onaverage 6500 times a day. Presenting the data in this way, stillshows support for the hypotheses outlined above, which in turnshows that the prevalence with which videos produce a particularemotional response does not influence the extent to which the vid-eos are then shared.

Finding 3 – Prevalence is not related to degree of sharing.Finding 4 – Videos that elicit high arousal emotions are sharedabout twice as much as those that elicit low arousal emotions.Therefore hypothesis 1 is supported.Finding 5 – Non-Commercial videos typically gain more sharesthan commercial videos.

The conclusion that arousal more so than valence drives sharingis consistent with the findings of Berger and Milkman (2012) andthe views of Porter and Golan (2006) and Brown et al. (2010)who assert that more provocative material is more likely to beshared. We found that valence also has an impact on whethervideo content will be shared, with videos that elicited positiveemotions (regardless of degree of arousal) being shared 30% morethan videos that elicited negative emotions.

Finding 6 – Positive videos are shared 30% more than negative vid-eos. Therefore hypothesis 2 is supported.

However while valence does play a role, it would seem that itdoes so to a lesser extent than arousal (given that the sharing dif-ferences are greater for arousal comparisons than for valence com-parisons). We also found that while the non-commercial data thatdrew a positive emotional response from its audience did gainmore shares than those non-commercial videos that drew negativeemotional responses, the difference was not statistically significantfor the commercial videos. This does suggest that hypothesis 2 issupported, but it is not as strongly supported as hypothesis 1. Toconsider the combined effect of arousal (high, low) and valence(positive, negative) on average shares per day, a two-way (4 � 2)ANOVA was used across both the commercial and non-commercialvideos. The data reveal that while there was a significant main ef-fect for arousal (non-commercial: F (1, 796) = 17.08, p < .001, com-mercial: F (1, 794) = 4.04, p < .01), valence was not significant (non-commercial: F (1, 796) = 3.44, p > .05, commercial: F (1, 794) = .677,p > .05). Additionally we find no significant interaction betweenarousal and valence (non-commercial: F (1, 796) = .017, p > .05,commercial: F (1, 794) = .788, p > .05).

While the role of valence is not as substantial as that of arousaland is not statistically significant, the patterns consistent acrossboth data sets suggests that marketers should create content thatis positive, rather than negative if they wish for that content tobe shared with a wider network given the direction of our results.The conclusion that valence plays a role, but to a lesser degree isconsistent with the work of Berger and Milkman (2012). It is how-ever counter to both the research on the sharing of social experi-ences (rather than video content) and the work of Eckler andBolls (2011) who suggest valence plays a significant role.

In consideration of the final hypotheses, we found that videosthat elicited both high arousal and positive emotions were shared30% more on average than videos in the next closest arousal/valencegroup (HaN). However, this difference in sharing between the higharousal positive videos and any other video is not statistically signif-icant. This suggests that rather than a combined impact of arousaland valence, it is arousal (regardless of valence) that has the biggestimpact on the extent to which video content will be shared.

Finding 7 – Videos that elicit high arousal positive emotions areshared 30% more than those that elicit any other emotionalresponse, however, this difference is due to the individual impactof arousal rather than a combined impact of arousal and valence.Therefore hypothesis 3 is rejected.

6. Conclusion, implications and limitations

Little is known about the characteristics of successful viral vi-deo content. This research adds to the small pool of knowledge.We find that an audience’s emotional reaction to video contentdoes affect their propensity to share that material, but only whenthe emotion is highly arousing. While we find that designing higharousal content (particularly commercial content) is rare andtherefore difficult, it is this material that is most likely to be shared.Given this rarity, high arousal content will likely take longer toproduce and come at a cost to the advertiser. While creating a po-sitive emotional response over a negative one is still recom-mended, it is less important that the emotion felt be a positiveone, than that it should be strongly felt. While some videos thatelicited a negative emotional response from their audience did gainreasonable levels of sharing (such as sadness and disgust), it is per-haps a brave brand manager who would venture into this highlyprovocative negative space. While there is some evidence that‘norm violations’ (those considered offensive and outside accept-able behaviour) in commercial advertising rank well against mea-sures of attention, recall and recognition (Dahl et al., 2003), little isknown about the long term consequences of highly provocativenegative appeals on the brand. In addition, we find no evidenceof an interactive effect between arousal and valence, suggestingthat delivering content that elicits a strong emotional reactionshould be the main aim of content generators, regardless of the va-lence of the message.

K. Nelson-Field et al. / Australasian Marketing Journal 21 (2013) 205–211 211

With all this said, even if a marketer does manage to produce avideo that is highly arousing this is still no guarantee it will gotruly viral. The reality is most videos are not runaway viral suc-cesses, even videos coded in our study as being of highly arousingquality varied in the degree to which they were shared. The bestguarantee we can offer marketers is that highly arousing contentwill at least share more than low arousal content and will have abetter chance at going viral. This strongly suggests there is moreto the sharing puzzle. One limitation of this, and many other stud-ies in the area, is the singular focus on creative characteristics. Sucha focus is largely driven by the ease to which marketers can controlthe creative elements - human motivation to send and receive, lessso. But one area that is also under the control of marketers is seed-ing and optimization tactics. What are the synergies between a vi-deo that is highly arousing AND well seeded? A typical consumerbrand needs both ‘good advertising’ and ‘physical availability’(Sharp, 2010), could the physical distribution of a video be equallyas important as the creative in its ability to go viral? Future re-search needs to consider creative and non-creative factors togetherto understand the relative importance of each element.

For now, this research offers marketers robust and generalisablefindings relating to which emotions they should strive for whencreating content. Marketers should focus less on creative appealand more on emotional appeal. While this may seem obvious, oftenthe obvious is overlooked – given the very large majority of a verylarge sample of videos were of low arousal nature, and given veryfew marketers would go out of their way to create mediocre con-tent, this is clearly the case here. Either they did not brief theiragency or they failed to adequately pre-test and relied on theirown judgment of arousing.

Acknowledgments

We wish to thank Unruly Media for their data contribution,without which this study would not have been possible.

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