ArticlePDF Available

Emotion and Virality: What Makes Online Content Go Viral?



"Companies are relying more and more on online communication to reach consumers. While some viral campaigns are tremendously successful, others remain far below expectations. But why are certain pieces of online content more viral than others? An analysis conducted on the New York Times’ most-emailed list, along with further experimental evidence, showed that positive content is more viral than negative content. However, the relationship between emotion and social transmission is more complex than valence alone. Virality is driven, in part, by activation and arousal. Content that evokes either high-arousal positive emotions (awe) or negative emotions (anger or anxiety) tends to be more viral. Content that evokes low arousal or deactivating emotions (e.g., sadness) tends to be less viral. These results were also true when examining how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content is featured). Taking the effect of emotions into account helps to design effective viral marketing campaigns. "
/ / /
Emotional aspects of
content impact on
whether it is shared.
19Insights / Vol. 5, No. 1, 2013, pp. 18 – 23 / GfK MIR
Is virality random? ///
One of the most popular online
videos of all time is “Charlie bit me”, a short film about two
little boys. They are sitting side by side in a chair when Charlie,
the younger brother, mischievously bites down rather hard
on his older brother Harry’s finger. Harry isn’t sure whether to
laugh or cry while baby Charlie is unmistakably delighted by
his little trick. Nothing much happens in the video, and yet the
clip had received more than 400 million views on YouTube by
the end of 2011.
But why did this video go viral? And more generally, why are
certain pieces of online content more viral than others? The
emergence of social media (e.g., Facebook and Twitter) has
boosted interest in word-of-mouth and viral marketing. But
while it is clear that consumers often share online content,
and that social transmission influences product adoption and
sales, less is known about why consumers share content or
why certain content becomes viral.
Why people share content ///
In fact, there are many
reasons why people enjoy exchanging content. One reason
why people share stories, news, and information is because of
the useful information contained. Coupons or articles about
good restaurants help people save money and eat more
healthily. Consumers may forward such practically useful con-
tent to help others or to appear knowledgeable and enhance
their self-image. Others might share practically useful content
because they hope to obtain equally useful information from
their friends in return.
Emotional aspects of content may also impact upon whether it
is shared. People discuss many of their emotional experiences
with others, and there is evidence that extremes of satisfac-
tion (highly satisfied or highly dissatisfied consumers) gener-
ate more word-of-mouth than average experiences.
Viral Marketing, Word-of-Mouth,
Social Transmission, Emotions, Online Content
the authors
Jonah Berger,
Joseph G. Campbell Assistant Professor of Marketing,
Katherine L. Milkman,
Assistant Professor of Operations and
Information Management,
Both at the Wharton School,
University of Pennsylvania,
Philadelphia, USA
Emotion and Virality:
What Makes Online Content Go Viral?
Jonah Berger and Katherine L. Milkman
doi 10.2478 / gfkmir-2014-0022
20 GfK MIR / Vol. 5, No. 1, 2013, pp. 18 23 / Insights
Content characteristics matter
Positive beats negative /// Common sense, as well as
simply considering the kind of information that is broad-
casted on news channels, suggest that people tend to pass
along negative news more than positive news. But the results
of a study conducted on the New York Times and its list of
most shared articles indicate that positive news actually tends
to be more viral (Box 1). Affect-laden content (independent
of valence) is more likely to make the most-emailed list than
content that does not evoke emotions. In fact, content is more
likely to become viral the more positive it is. In other words,
while either more positive or more negative content tends
to be more viral than content that does not evoke emotion,
positive content is generally more viral than negative content.
High-Arousal emotions favor sharing /// When taking
into account specific emotions, it becomes evident that the
role of emotion in transmission is more complex than mere
valence alone. While awe-inspiring (positive) content is more
viral and sadness-inducing (negative) content is less viral,
some negative emotions are positively associated with viral-
ity. Anxiety and anger-inducing stories are both more likely
to make the most-emailed list after controlling for an article’s
valence and emotional intensity. This suggests that trans-
mission is about more than just sharing positive things and
not sharing negative ones. Content that evokes high-arousal
emotions (i.e., awe, anger, and anxiety) after accounting for
valence is more viral.
{ Box 1 }
New yoRK TIMes aRTIcles
The objective of this study was to investigate
which types of New York Times articles were highly
shared. A webcrawler visited the Times’ homepage
( every 15 minutes from 30August
to 30 November 2008 and recorded information about
every article (approximately 7,000 articles in total) on
the homepage as well as on each article on the most-
emailed list (updated every 15 minutes). The dataset
captured information on several potentially relevant
characteristics such as topic area, location in the news-
paper and author fame. Of all of the articles, 20 %
earned a position on the most-emailed list.
To document the emotional content of each article,
automated sentiment analysis was used to quantify
the positivity (i.e., valence) and emotionality (i.e.,
affect-ladenness) of each article. A computer program
(LIWC) counted the number of positive and negative
words in each article using a list of 7,630 words that
were classified as either positive or negative. Positiv-
ity was quantified as the difference in percentage
between the positive and negative words in an arti
cle. Emotionality was quantified as the percentage of
words classified as either positive or negative.
Human coders were necessary to classify the extent to
which content exhibited more specific characteristics,
in particular anger, anxiety, awe or sadness. There was
a closer focus on negative emotions because they are
easier to differentiate and classify than positive emo-
tions. The coders also distinguished whether articles
contained practically useful information, or whether
they evoked interest or surprise (control variables).
Only a random subset (approx. 2,500) of the articles
were subject to human coding. A logistic regression
model was applied to estimate the influence of the
Content that evokes high-arousal
emotions like awe, anger or anxiety
is more viral.
21Insights / Vol. 5, No. 1, 2013, pp. 18 – 23 / GfK MIR
What else favors virality? /// Articles that are more
interesting, informative (practically useful) and surprising
are more likely to make the Times’ most-emailed list. Simi-
larly, being featured for longer in more prominent positions
on the Times homepage (e.g., the lead story as opposed
to at the bottom of the page) is positively associated with
making the list. However, the relationship between the
emotional characteristics of content and virality holds even
when these content characteristics are taken into account.
Therefore, the correlation between stories that evoke certain
emotions and their higher virality is not simply down to edi-
tors featuring those types of stories, thereby mechanically
increasing their virality.
The same is true for longer articles, articles by more famous
authors and articles written by women. These are also more
likely to make the most-emailed list, but the emotional effect
can still be observed above and beyond these other effects.
The results also hold true independent of an article’s general
topic (according to 20 areas classified by the Times such as
Science or Health). This indicates that they are not merely
driven by certain areas tending to be the ones to both evoke
certain emotions and be particularly likely to make the most-
emailed list. The observed relationships between emotion
and virality hold true not only across topics but also within
them. Even among opinion or health articles, for example,
awe-inspiring articles tend to be more viral.
Figure 1 shows how different characteristics shape the
virality of an article. Virality is driven by more than just
valence. Sadness, anger, and anxiety are all negative emo-
tions, but while sadder content is less viral, content that
evokes anxiety or anger is actually more viral. Positive and
negative emotions characterized by activation or arousal
(i.e., awe, anxiety, and anger) are positively linked to virality,
while emotions characterized by deactivation (i.e., sadness)
are negatively linked.
figure 1:
Percentage change in the probability of making the most-emailed list
due to a one standard deviation increase in various article traits
Practical Value
Time at Top of Homepage
34 %
25 %
– 16 %
30 %
30 %
21 %
13 %
18 %
14 %
20 %
22 GfK MIR / Vol. 5, No. 1, 2013, pp. 18 23 / Insights
External drivers of attention (e.g., being prominently fea-
tured) shape what becomes viral, but content characteris-
tics are of similar importance. For instance, a one standard
deviation increase in the amount of anger an article evokes
increases the odds that it will make the most-emailed list
by 34 % (Figure 1). This increase is equivalent to spending
an additional 2.9 hours as the lead story on the Times web-
site, which is nearly four times the average number of hours
articles spend in that position. Similarly, a one standard
deviation increase in awe increases the odds of making the
most e-mailed list by 30 %.
Testing the high-arousal-emotions effect in a marketing
context /// Two follow-up experiments confi rmed the
results of the New York Times fi eld study in two different
and more marketing-related contexts. The experiments used
different versions of stories to test how different amounts
of amusement (positive emotion) or anger (negative emo-
tion) infl uenced arousal and sharing. Participants said they
would be more likely to share an advertising campaign when
it induced more amusement. They also said that they would
be more likely to share a customer service experience when
it induced more anger. In both cases the higher willingness to
share was driven by the arousal it evoked.
Rather than targeting “special” people,
it may be more beneficial
to focus on crafting contagious content.
Participants said they would be more likely to share an
advertisement if they faced the high as opposed to low
amusement/anger advertisement. The results were similar
for arousal: the high amusement/anger condition evoked
more arousal than the low amusement/anger advertisement.
Marketing Implications /// When looking to generate
word-of-mouth, marketers often try targeting “infl uentials”
or opinion leaders. But while this approach is pervasive, its
value and cost-effectiveness is doubtful. Rather than tar-
geting “special” people, it may be more benefi cial to focus
on crafting contagious content. The study results illuminate
how content characteristics can improve virality.
Amuse rather than relax your audience ///
ndings shed light on how to design successful viral
marketing campaigns and craft contagious content.
While marketers often produce content that paints their
product in a positive light, content is more likely to be
shared if it evokes high-arousal emotions. Ads which
make consumers content or relaxed, for example, will not
be as viral as those which amuse them.
> Negative emotions do no harm when they activate ///
Further, while some marketers might shy away from ads
that evoke unpleasant feelings, negative emotions can
actually increase transmission if they are characterized
by activation. BMW, for example, created a series of short
online fi lms called “The Hire” that they hoped would go
viral. The series included car chases and story lines that
often evoked anxiety (with titles such as “Ambush” and
“Hostage”). While one might be concerned that negative
emotion would hurt the brand, the study results suggested
that it would increase transmission because anxiety induces
arousal (incidentally, “The Hire” was highly successful, gen-
erating millions of views).
> Sadness hinders transmission /// According to the
study, sadness is not a good vehicle for viral communi-
cation initiatives. While emotions that generate activation
and arousal favor sharing, no matter whether they are pos-
itive or negative, sadness actually has a negative impact
on willingness to share, no matter whether the induced
condition of sadness is low or high. Therefore, public health
information, for example, is more likely to be passed on if
it is framed to evoke anger or anxiety rather than sadness,
which is more frequently encountered.
23Insights / Vol. 5, No. 1, 2013, pp. 18 – 23 / GfK MIR
Berger, Jonah (2011),
“Arousal Increases Social Transmission of Information”,
Psychological Science, 22(7), 891 – 893.
Berger, Jonah (2013):
“Contagious: Why Things Catch On”; Simon & Schuster
Cashmore, Pete (2009),
“YouTube: Why Do We Watch?”
Godes, David and Dina Mayzlin (2009),
“Firm-Created Word-of-Mouth Communication:
Evidence from a Field Test”,
Marketing Science, Vol. 28, pp. 721 – 739.
> Angry consumers’ online actions might be critical
/// Similar points apply to managing online consumer
sentiment. While some consumer-generated content (e.g.,
reviews and blog posts) is positive, much is also negative,
and can lead to consumer backlash if it is not carefully
managed. Mothers who were offended by a Motrin ad
campaign, for example, banded together and began post-
ing negative YouTube videos and tweets. While it is impos-
sible to address all negative sentiment, certain types of
negativity may be more important to address because
they are more likely to be shared. Customer experiences
that evoke anxiety or anger, for example, are more likely
to be shared than those that evoke sadness (and textual
analysis can be used to distinguish different types of
posts). Consequently, it may be more important to rectify
experiences that make consumers anxious rather than
Managerial Summary of an article published in
the top academic journal “Journal of Marketing
Berger, Jonah; Milkman, Katherine L. (2012):
“What Makes Online Content Viral?”, Journal of
Marketing Research, Vol. 49, No. 2 (April), pp. 192 – 205.
... The type of content which is shared varies in this case, some contents might be related to emotions whereas it might also be the case that it is shared because of influencers or celebrities. For instance, emotions (such as awe, anger, and anxiety) that contain high arousal make content viral rather than low-arousal emotions(sadness), moreover it is also true for those content that contains surprising or informatic information (Berger & Milkman, 2013). On the other hand, false news that triggers emotions (such as fear, disgust, and surprise) spreads more widely and fastly than the true news which triggers emotions like anticipation, sadness, joy, and trust (Vosoughi et al., 2018). ...
... High arousal emotions [t(89) = 2.30, p = .02] (Berger, 2011) or positive emotions (Berger & Milkman, 2012;Tellis et al. 2019) such as (awe, anxiety etc) tend to spread more (Berger & Milkman, 2013). Physiological arousal is somewhat related to information diffusion, high-arousal positives ( Guadagno et al. 2013;Guede et al. 2017;Nelson-Feld et al, 2013) such as affection, awe (Nikolinakou & King 2018), or negative such as anger, or anxiety emotions is more viral than the content that provokes low-arousal, or deactivating, emotions such as sadness (Berger, 2011). ...
... It is surprising to see that type of user triggers the spread of content/news. Based on gender, content by female or female authors tend to be more viral (Berger & Milkman, 2013;Milkman and Berger, 2012). Meanwhile posts spread fastly, widely, and farther which are generated by the hateful users compared to normal users (Mathew et al. 2019 ...
Full-text available
Technical Report
Online information is now going viral in an exponential fashion. However, information spread suddenly. Often there are movies, memes, news, or videos that go viral suddenly. For example, in any kind of pandemic, various kinds of news (true or fake) go viral which are correlated with that particular situation. Did you ponder what makes them go viral?. To find the answer of the question above and have a better understanding of online virality a systematic literature review is conducted. Included papers were between the span 2004-2020. Based on our outcomes, we found that several factors like type of emotion, news, content characteristics, attachments impact virality. Lastly, we suggest possible tactics for influencers, politicians, and marketers to make their contents or posts go viral.
... Online content goes viral when it plays with strong emotions (77). Health topics are, by nature, prone to elicit feelings of uncertainty, fear, anger, unfairness, excitement, hope and curiosity about things that are very particular and important to individuals. ...
Full-text available
Technical Report
Noncommunicable disease-related, and health misinformation is a growing concern as more and more individuals obtain their health information from digital venues such as search engines or social media platforms. While increased access to information on health issues can be seen as generally positive, the spread of inaccurate medical information is of course problematic. It can lead to harmful lifestyle or dietary choices, self-medication, the abandonment of medical treatment and incorrect diagnoses. As such, three meetings were hosted to discuss the topic with representatives from Member States, the media and social media sectors, and civil society. The outcomes of these meetings are reflected in this Toolkit. This Toolkit was drafted following these meetings, and includes the concerns, challenges and conclusions shared during those conversations by all discussion partners. It is the product of an intense iterative process, of arguments between competing views and interests, and of the constant upgrades in available knowledge. It reflects, to the extent possible, the developments that occurred after the meetings, but it should be read with the knowledge that it does not presume to contain everything there is to know about this topic.
... We know the established factors that help OSN network propagation (Javed et al. 2020;Berger and Milkman 2013;Sanzgiri et al. 2012). However, these factors do not account for the connectivity of the network and may not be effective vectors for disruption. ...
Full-text available
This paper tests disruption strategies in Twitter networks containing malicious URLs used in drive-by download attacks. Cybercriminals use popular events that attract a large number of Twitter users to infect and propagate malware by using trending hashtags and creating misleading tweets to lure users to malicious webpages. Due to Twitter’s 280 character restriction and automatic shortening of URLs, it is particularly susceptible to the propagation of malware involved in drive-by download attacks. Considering the number of online users and the network formed by retweeting a tweet, a cybercriminal can infect millions of users in a short period. Policymakers and researchers have struggled to develop an efficient network disruption strategy to stop malware propagation effectively. We define an efficient strategy as one that considers network topology and dependency on network resilience, where resilience is the ability of the network to continue to disseminate information even when users are removed from it. One of the challenges faced while curbing malware propagation on online social platforms is understanding the cybercriminal network spreading the malware. Combining computational modelling and social network analysis, we identify the most effective strategy for disrupting networks of malicious URLs. Our results emphasise the importance of specific network disruption parameters such as network and emotion features, which have proved to be more effective in disrupting malicious networks compared to random strategies. In conclusion, disruption strategies force cybercriminal networks to become more vulnerable by strategically removing malicious users, which causes successful network disruption to become a long-term effort.
... Very few studies consider features like readability and emotion. A notable work in this regard would be that of Kate et al. [15] and Berger and Milkman [16], where, the importance of readability and emotion in content virality is studied. Unlike their work, this work explores NRC emotion lexicon [17], which is not yet used to study the effect of emotions on news popularity prediction before publication by extracting emotions in the news content. ...
Full-text available
The development of world wide web with easy access to massive information sources anywhere and anytime paves way for more people to rely on online news media rather than print media. The scenario expedites rapid growth of online news industries and leads to substantial competitive pressure. In this work, we propose a set of hybrid features for online news popularity prediction before publication. Two categories of features extracted from news articles, the first being conventional features comprising metadata, temporal, contextual, and embedding vector features, and the second being enhanced features comprising readability, emotion, and psycholinguistics features are extracted from the articles. Apart from analyzing the effectiveness of conventional and enhanced features, we combine these features to come up with a set of hybrid features. We curate an Indian news dataset consisting of news articles from the most rated Indian news websites for the study and also contribute the dataset for future research. Evaluations are performed over the Indian news dataset (IND) and compared with the performance over the benchmark mashable dataset using various supervised machine learning models. Our results indicate that the proposed hybrid of enhanced features with conventional features are highly effective for online news popularity prediction before publication.
... Moreover, another fingerprint of misinformation is its reliance on emotions (Bakir & McStay, 2018;Kramer et al., 2014;Martel et al., 2019;Taddicken & Wolff, 2020). In general, content that evokes high-arousal emotions is more viral (Berger, 2011;Berger & Milkman, 2009;Berger & Milkman, 2013;Goel et al., 2015;Milkman & Berger, 2014), which explains why social networks are a source of massive-scale emotional contagion (Fowler & Christakis, 2009;Kramer et al., 2014;Rosenquistet al., 2011). One of the main reasons proposed to explain this behavior is the dualprocess theory of judgment stating that emotional thinking (in contrast to a more analytical thinking) hinders good judgment (Evans, 2003;Stanovich, 2005). ...
Full-text available
Not all misinformation is created equal. It can adopt many different forms like conspiracy theories, fake news, junk science, or rumors among others. However, most of the existing research does not account for these differences. This paper explores the characteristics of misinformation content compared to factual news—the “fingerprints of misinformation”—using 92,112 news articles classified into several categories: clickbait, conspiracy theories, fake news, hate speech, junk science, and rumors. These misinformation categories are compared with factual news measuring the cognitive effort needed to process the content (grammar and lexical complexity) and its emotional evocation (sentiment analysis and appeal to morality). The results show that misinformation, on average, is easier to process in terms of cognitive effort (3% easier to read and 15% less lexically diverse) and more emotional (10 times more relying on negative sentiment and 37% more appealing to morality). This paper is a call for more fine-grained research since these results indicate that we should not treat all misinformation equally since there are significant differences among misinformation categories that are not considered in previous studies.
Full-text available
This chapter presents the concept of Playful Publics on TikTok. In May 2021, the popular video-sharing platform became a powerful agent in the Israel-Gaza warfare giving rise to various participatory behaviors cultivated by TikTok’s culture of memes. The platform’s recommendation feed (“For You”) was swamped with memetic videos created by Palestinian and Israeli TikTokers who participated within the platform’s performative framework for content creation - the #challenge. As a driver of virality, the #challenge is a play-based collaborative task governed by a set of performative rules in which users are encouraged to coopt a competitive mission initiated by randoms. A multimodal analysis identified unique #challenge videos harnessing playful vernaculars of resistance, resulting in two visibility strategies. The first was the artistic use of features like the duet challenge (#StandUp), where a Palestinian TikToker “hijacked” an Israeli video of a soldier lip-syncing national songs while calling others to imitate his version of the video to articulate a political claim against the “stolen lands.” The second was a violent and mobilized manifestation of TikTok’s rivalrous mechanism of play resulting in the #Hit&Run challenge inviting both sides in a terrorizing call-to-action to hit and run random Israelis\Palestinian in the streets of Israel. Those trends are part of #challenges showing how networked crowds on TikTok are rendered into Playful Publics with algorithmic motivation to translate their social-political sentiments into memetic structures, becoming both weapon and medium in a battlefield dominated by Playful vernaculars of content creation. TikTok’s affordance of play calls for further interrogation while pushing Playful Publics to extend normative frameworks and revise moral questions dealing with their present and future online participatory cultures.
Persistent gender stereotypes portray women as pleasant and polite, but in the wake of the #MeToo movement and polarized politics, female candidates are turning to Twitter and they aren’t hiding their frustration. Congressional candidates use Twitter to connect with voters, but political stalemates over health care, reproductive rights, and pay equity are the fodder for female candidates’ emotionally charged rhetoric on Twitter. Women are running and winning at rates comparable to men, but female candidates are relying on emotional appeals in distinct ways from their male counterparts. We use a dataset of tweets by candidates for the U.S. House from 2016–2020 to evaluate gender-based differences in the emotional appeals candidates make on Twitter. We find that women running for office adopt a unique style of angry emotional appeals on Twitter, as female candidates defy stereotypes by incorporating more angry rhetoric in their tweets. These differences persist after accounting for differences in party, electoral success, district competitiveness, and other potential confounds. Our research demonstrates that women seeking congressional office act differently than men in their self-presentation online, and offers insight into how anger has become central to online messaging.
In a so-called post-truth era, research on the subject of the spread of mis- and disinformation is being widely explored across academic disciplines in order to further understand the phenomenon of how information is disseminated by not only humans but also the technology humans have created (Tandoc, Sociol Compass 13(9), 2019). As technology advances rapidly, it is more important than ever to reflect on the effects of the spread of both mis- and disinformation on individuals and wider society, as well as how the impacts can be mitigated to create a more secure online environment. This chapter aims to analyse the current literature surrounding the topic of artificial intelligence (AI) and the spread of mis- and disinformation, beginning with a look through the lens of the meaning of these terms, as well as the meaning of truth in a post-truth world. In particular, the use of software robots (bots) online is discussed to demonstrate the manipulation of information and common malicious intent beneath the surface of everyday technologies. Moreover, this chapter discusses why social media platforms are an ideal breeding ground for malicious technologies, the strategies employed by both human users and bots to further the spread of falsehoods within their own networks, and how human users further the reach of mis- and disinformation. It is hoped that the overview of both the threats caused by and the solutions achievable by AI technology and human users alike will further highlight the requirement for more progress in the area at a time when the spread of falsehoods online continues to be a source of deep concern for many. This chapter also calls into question the use of AI to combat issues arising from the use of advanced Machine Learning (ML) methods. Furthermore, this chapter offers a set of recommendations to help mitigate the risks, seeking to explore the role technology plays in a wider scenario in which ethical foundations of communities and democracies are increasingly being threatened.
Indeed, due to the rapid advances of the times and the development of the Internet, the way in which marketing is addressed today has changed and consumers have the opportunity to “generate” their own information about products and brands, as well as to spread and share it through copying, browsing and etc. Consumers 'purchasing decisions are more susceptible, as the increase in ways of gaining and generating multitudinous information and some might be deceptive—because of the decreasing cost of sharing, the content and quality of consumer-generated marketing messages are becoming increasingly difficult to control as a result. Emotion acts as one of the factors contributing to changes in behaviors and actions, has been widely studied in the past few decades [9]. Realistically, the marketing messages that are spread by consumers seem to share a common characteristic of trying to achieve communication by evoking emotional resonance in consumers. We found that emotion will not only help to explore and reveal the relationship between the emotionality of the message content and the sharing behavior but also provides more valuable and relevant suggestions and insights for enterprises to carry out new media marketing, as it distinguishes marketing messages from ordinary messages [2].KeywordsEmotional marketingBusiness managementCoca-Cola
Full-text available
Purpose This study uses TikTok as a novel medium to extend the literature on online activism. It adds to the emergent body of knowledge about playful political participation among youth. It also explores how creative micro-videos can be a force to create momentum and shape opinions around social and political topics. Design/methodology/approach A content analysis of 203 TikTok videos reflecting the ongoing Israeli-Palestinian conflict that took place in Sheikh Jarrah in 2021, was used to understand the extent to which TikTok’s platform’s affordances were used and examine the message frames that emerged when online users disseminated messages of affect and solidarity with the Palestinians during the Sheikh Jarrah incident. Findings The study showed that TikTok affordances encourage virality and creative crafting of direct and indirect political content, making the platform a space for political expression, mobilization, and online activism. The affordances used during the TikTok Intifada were visibility, editability, association, and persistence. The two most prominent frames were the use of hashtags to promote the cause, followed by direct political content. Practical implications Researchers are given guidance on how TikTok design elements are now allowing a very young segment of users to become producers of political content in a way where messages are creatively crafted using the platform’s affordances. Social implications This study captured social media activism among a young segment of users on a playful platform. Youth are now able to raise awareness and call to action by capitalizing on platform affordances to create and spread content about a cause. Originality/value Using the connective-collective approach, this work adds a unique dimension to the literature on how TikTok is becoming a novel space for the emergence of grassroot movements among a very young segment of users and how hard political content has been adapted to fit the playful nature of this dynamic platform. The work also takes lead into studying the Palestinian Intifada in a virtual context, where an unusual activism frame emerged due to the nature of TikTok as a micro-video and innovative platform.
Abstract In this paper, we investigate the effectiveness of the firm’s proactive management of customer-to-customer communication. We are particularly interested in understanding how, if at all, the firm should go about effecting meaningful word-of-mouth. To tackle this problem, we collect data from two sources: 1) We implemented,a large-scale field test in which a national firm created word of mouth,through,two populations: customers and non-customers, 2) We collected data from an online experiment. We break our theoretical problem into two subproblems. First, we ask, “What kind of WOM drives sales?” Motivated by previous research, we hypothesize that for a product with a low initial awareness level, the WOM that is most effective at driving sales is created by less-loyal (not highly-loyal) customers, and occurs between acquaintances (not friends). We find support for this in the field test as well as in an experimental,setting. Hence we demonstrate the potential usefulness of exogenously-created WOM: conversations,are created where,none would naturally have occured otherwise. Then, we ask, “Which agents are most effective at creating this kind of WOM?” In particular, we are interested in evaluating the effectiveness of the commonly-used opinion leader designation. We find that while opinion leadership is useful in identifying potentially effective spreaders of WOM among very loyal customers, it is less useful for the sample of less loyal customers. Keywords: Word of Mouth, Promotion, Advertising 1I ntroduction
Social transmission is everywhere. Friends talk about restaurants, policy wonks rant about legislation, analysts trade stock tips, neighbors gossip, and teens chitchat. Further, such interpersonal communication affects everything from decision making and well-being (Asch, 1956; Mehl, Vazire, Holleran, & Clark, 2010) to the spread of ideas, the persistence of stereotypes, and the diffusion of culture (Heath, 1996; Heath, Bell, & Sternberg, 2001; Kashima, 2008; Schaller, Conway, & Tanchuk, 2002; Schaller & Crandall, 2004). But although it is clear that social transmission is both frequent and important, what drives people to share, and why are some stories and information shared more than others?
  • Jonah Berger
Berger, Jonah (2013): "Contagious: Why Things Catch On";