Conference PaperPDF Available

Social media – An arena for venting negative emotions

Harri Jalonen
Dr, Adjunct professor, Turku University of Applied Sciences, Finland
Phone: +358 44 907 4964
Social media is seen as transforming into a global multiplier through which emotional experiences are shared and
strengthened. The essential factor in the ongoing transformation is that although emotions are felt on an individual
level, in social media they can simultaneously be shared with and by others. Many studies have shown that social
media is an arena for sharing information that reflects negative emotions. The theme of the paper is important as
nowadays people have access to online discussions, blogs and even websites devoted entirely to sharing negative
emotional experiences. After reviewing the literature, the paper explores and discusses the implications of negative
emotions shared in social media. The main contribution of the paper is the anatomy of the diffusion of collective
negative emotion in social media. In addition, the paper discusses the positive consequences of negative emotions
from an organisation’s perspective.
Social media has changed the behavior inside and outside the organisation. It has provided new
opportunities and posed new threats. A positive interpretation of social media draws on the thought that
social media has provided new possibilities to the internal use of external knowledge as well as to the
external exploitation of internal knowledge. This has meant significant improvements particularly in
leadership, innovation management, knowledge management, marketing communication, and customer
service. Studies have shown that social media has made organisations transparent in unparalleled way. In
addition to positive and anticipated consequences, it has also been identified side-effects effects such as
the loss of control and power to manage organisation´s public image (Li & Bernoff, 2011). The more
open and social organisations has become, the more vulnerability they has become too (Mangold &
Faulds, 2009). The odds of brand insults and the loss of confidential information have increased
tremendously. Ironically, it may also happen as Denyer et al. (2011) have pointed out that social media
can be used for political purposes by managers implying that social media is no more ‘social’, ‘open’ or
‘participatory’ than other communication methods.
Social media is creeping into many aspects of our lives. A bit pointedly, it can be argued that many
behaviors that sociologists study are nowadays taking place in online. Social media is not an alternative to
real life, but it is part of it. Social media is still relatively new phenomenon which consequences cannot
be fully predicted. However, some sophisticated guesses can be made. The one is that behaviour in social
media contradicts with the theory of gatekeeping. According to the theory which was originally
developed by Kurt Lewin (1943), gatekeeping is the process through which information is filtered for
dissemination. Every medium has gatekeepers, who select and confine the information flows. Reporters,
for example, decide which sources are chosen to include in a story, whereas editors decide whether stories
are printed or covered. In contrast to traditional mass communication, social media is unregulated context
allowing ordinary people to publish almost anything that come to their minds. There is no need room for
gatekeepers in social media. Unsurprisingly, many studies have shown that social media is an arena for
sharing information that reflects negative emotions (e.g. Lapidot-Barak-Lefler & Barak, 2012; Hughes et
al., 2012; Lee & Cude, 2012).
The study focusing on collective negative emotions in social media is important because people have
nowadays access to online discussions, blogs and even Web sites devoted entirely to sharing negative
emotional experiences (Jones, 2009). Whether that mirrors “information democracy” (Sawhney & Kotler,
2001) or not, a possibility to ventilate feelings online poses a huge challenge for organisations.
Upon reviewing the literature, the paper explores and discusses the implications of negative emotions
shared in social media. The paper proposes the anatomy of the diffusion of collective negative emotion in
social media. In addition to detrimental consequences, it is supposed that negative emotions may have
positive effects.
Social media refers herein to a constellation of Internet-based applications that derive their value from the
participation of users through directly creating original content, modifying existing material, contributing
to a community dialogue and integrating various media together to create something unique (Tapscott &
Williams, 2007; Kaplan & Haenlein, 2010). Emotion is defined as feeling state involving thoughts and
physiological changes, outward expressions such as facial reactions, gestures or postures (Brehm, 1999;
Cacioppo & Gardner, 1999). Emotion has an object at which it is intuitively or intentionally directed
(Ibid.). Adapting Bar-Tal et al. (2007) and Schweitzer & Garcia (2010), the paper focuses on collective
emotions, which are shared by large number of individuals who are not necessarily members of the same
group or society. Collective emotions can display new properties, which are more (or less) than the
aggregation of emotions felt by individuals. The paper adopts the view that textual communication can
used for evaluating emotions (cf. Jansen et al., 2009; Chmiel et al., 2011). Social media posts are seen
acts which are, at least, partly induced by emotions.
Psychological literature typically classifies emotions into two axes that describe their valence and arousal
(Fig. 1). Valence indicates whether the affect related to an emotion is positive or negative, and arousal
indicates the personal activity induced by that emotion (Russel, 1980; Schweitzer & Garcia, 2010).
‘Astonished’ is positive emotion that encourages action, whereas ‘satisfied’ although with positive
valence discourages action. ‘Annoyed’ refers to negative emotion that encourages action, whereas
‘disappointed’ means negative emotion that discourages action. The focus of this paper is primarily on
emotions with negative valence and positive arousal.
Figure 1. A circumplex model of affect (adopted from Russell, 1980).
Social media has provided organisations new ways to communicate (publish and share content),
collaborate (collectively create content), connect (network with other people and organisations), complete
(describing, adding or filtering information, tagging contents and showing a connection between contents)
and combine (mixing and matching contents) (Vuori, 2011). Social media extends organisations creating
new possibilities to engage with stakeholders both internally and externally. Through social media,
organisations can acquire inspiration from their customers, suppliers and other stakeholders. Social media
can also be used for getting to know consumer´s preferences and testing the ideas that are being
developed within the organisation before their launch on the market.
Figure 2. Social media changes organization internally and externally.
From this paper´s perspective, the most interesting is that social media enables direct two-way interaction
not only between organisation and customers but also within customers. It has been argued that marketing
for the Facebook generation demands both thinking and acting differently (Meadows-Klue, 2008). The
need for change holds true also within organisation as the distance between the manager and subordinate
has shortened dramatically. This paper suggests that social media forces organisations behave in a way
which inspires its people and customers emotionally. Kieztmann et al. (2011), for example, have
suggested that organisations should identify employees who can create content that is “emotionally
appropriate for the community”. Adapting Rubin (2011) social media is seen transforming into “one big
global amplifier through which emotional experience is transmitted and strengthened”. The essential thing
is that although emotions are felt on an individual level, in social media they can simultaneously be
shared to and by the others. This has wide range of “real life” consequences. It has been shown that
social, political, cultural and economic events are correlated with Twitter mood levels. Similarly, Gilbert
& Karahalios (2010) have found out that anxious expressions in social media could predict downward
pressure on the S&P 500 index.
It remains debatable whether the content of social media is more positive or negative. Previous studies
paint a contradictory picture. Robertson et al. (2013), for example, have found out that there are more
negative messages than positive ones in social media. Thelwall et al., (2010), among others, have come to
opposite conclusion. According to their study of MySpace messages, two-thirds of messages have
positive tone, and only one-thirds was negative.
Although there is no natural law, which proclaims that negative emotional experiences dominate social
media, however, this paper assumes that negative emotional experience and particularly its diffusion in
the community is based on logic which is different than in case of positive emotional experience. This is
because of negativity bias. Psychological studies have shown that negative experience, or fear of bad
events has a greater impact on people than do neutral or positive experiences (Baumeister et al., 2001).
Studies have also shown that negative events grow more rapidly with space or time than positive events.
This implies that negative emotions are more contagious than positive ones (Rozi & Royzman, 2001).
Negativity bias in online behaviour has been identified in a number of studies which has focused on
electronic word-of-mouth (eWOM). eWOM refers to a “statement made by potential, actual, or former
customers about product or company, which is made available to a multitude of people and institutions
via the Internet” (Hennig-Thurau et al., 2004). Lee & Cude (2012), for example, have found out that
consumers are very likely to use Internet as a mean to express their dissatisfaction. The stronger impact of
negative eWOM compared to positive eWOM has been explained by arguing that negative information is
more diagnostic than positive information in terms of cognitive judgement and decision making (Jones,
2009; originally Herr et al., 1991).
The paper views diffusion as the process in which an idea, thought or concept is communicated through
certain channels over time among the members of the social system (cf. Rogers, 2003). Instead of one-
way and linear process, it is assumed that the diffusion is a two-way and complex process in which
members involved affect to others and are being affected by others.
“United Breaks Guitar” (UBG) is probably one of the most popular examples of how negative emotional
experience can be diffused through social media. UBG is the song made and posted a YouTube by David
Caroll and his band Sons of Maxwell. It tells a story of how United´s baggage handling broke Caroll´s
guitar and how United refused to compensate the losses. The musical video was embedded into popular
BoingBoing blog whereafter it was given credit in Twitter community. After two weeks the musical video
was downloaded over 3.5 million times. (Hemsley & Mason, 2013).
This paper suggests that the UBG case is not an exception but a prevailing reality. It is expected that
despite of differences in nuances, UBG and similar incidents follow certain logic. Perhaps it is possible to
reveal the anatomy of the diffusion of collective negative emotion in social media.
A literature review of previous studies implicates six attributes of diffusion of collective negative emotion
in social media: 1) reasons to ventilate negative emotion, 2) clusterization of negative emotion, 3)
globallocal interplay, 4) nonlinear feedback, 4) possibility to anonymity, 5) key complainers, and 6)
emergent result.
Reasons to ventilate negative emotion
People express negative emotions online for a number of reasons. In consumer behavior research three
reasons have identified (Verhagen et al., 2013). Firstly, consumers ventilate for themselves. Thogersen et.
al., (2009) have found that consumers use negative eWOM for drawing attention to their dissatisfaction in
order to get solution or compensation. Secondly, consumers ventilate for helping others. This is case
when people disclosure their negative experiences in order to prevent others from suffering a similar
incident (Litvin et al., 2008; Parra-López et al., 2011). Thirdly, consumers ventilate for helping company
to improve their performance. Zaugg & Jäggi (2006), for example, have identified that consumers
complain “to assure that the issue is structurally solved”. It has also been suggested that sometimes people
run to rant-sites for venting anger (Martin et al., 2013). Posted rants may act as catharsis in sense that
people feel calm and relaxed after ranting (ibid.). Adapting Russell’s circumplex model all above
mentioned reasons for ventilating represent behavior which is motivated by emotionally negative valence
and positive (encouraging) arousal.
Clusterization of negative emotions
Instead of isolated experiences, what matter is their clusterization. Clusterization of emotion refers herein
to transformation of individual emotional states into cluster emotional states. Many studies imply that
emotion can bring people together. Bae & Lee (2012), for example, have found that the behavior of
popular Twitter users have an effect on their audiences’ moods. Metaphorically, clusterized emotions
constitute avalanches (Tadic et al., 2013) and groundswells (Bernoff & Li, 2008), which may have
detrimental effects on organisations. “United Breaks Guitar” music video was a negative avalanche which
hit the United Airlines. The root cause was mishandling of instrument, however, in order to become an
issue what was needed was other individuals’ contributions in terms of tweets, blog posts, comments etc.
Consistently with the negativity bias, Tadic et al. (2013) have found out that negative emotion valence
leads to the occurrence of larger avalanches than positive emotions. Presumably, the feature of social
media that allows a particular post to be available to everyone immediately increases the odds of
emotional bursts (Schweitzer & Garcia, 2009).
Globallocal interplay
Social media’s statistic is impressive. By the beginning of 2014, the number of users of popular social
media sites is counted in hundreds of millions. The leading social media site, Facebook, has gathered
1200 million users in ten years. Microblogging service Twitter has attracted over 500 million users since
its foundation in 2006. Even more rapidly has grown in 2009 founded instant messaging service
WhatsApp, which has claimed to have over 400 million active users. Sina Weibo, China´s biggest social
media site has gathered over 500 million users. Vkontake, popular social networking site in Eastern
Europe, particularly in Russia, is also growing rapidly having nowadays over 200 million users.
The numbers of users, even though impressive, is not the issue. Crucial thing is that a huge number of
users enable two processes: globalization of local events and localization of global events. Within social
media there is no lack of examples of how locally felt negative experience has transformed global issue.
“United Breaks Guitar” case and many similar incidents have shown that, in the age of social media, what
is local almost inevitably becomes global, whether the organisation wishes it to or not. The power has
been taken from organisations by the individuals and communities that create, share, and consume blogs,
tweets, and so forth (Kietzmann et al., 2011). The same obviously holds also true for opposite direction.
An incisive example is the discussion about the wholesome and safety of the gene manipulated food. It is
a global issue, which influences customer behaviour in local level. Twin forces of globalism and localism
are induced by the very nature of social media which removes time delays and physical distance.
Nonlinear feedback
Social media could be an effective form of two-way communication as it: “potentially closes the feedback
loop, or makes the loop smaller if you like, because it makes it easier for people to understand how they
can give their feedback” (Denyer et al., 2011). Feedback processes are non-linear i.e., minor changes
can produce disproportionately major consequences and vice versa. Feedback processes multiply the
connectivity inside and outside the organisation. A circular dependency relationship is typical of feedback
processes: this means that the result of the previous situation is the stake in the following one. In other
words, what has happened before is included, and continues, in what happens later. Many authors in the
field of social media have emphasised that most organisations have no choice: they cannot remain non-
participants, because their customers and other stakeholders participate anyway. Social media enables
customers to talk to one another and therefore multiplies the ability to express negative experiences (tähän
lähteitä). Avalanche, groundswell and eWOM originate from the same roots: a myriad of local
interactions between individuals bring about a chain of events that progress non-linearly. From the
perspective of negative emotions, the significance of the feedback processes promoted by social media
lies in that they enable the multiplication of small influential changes. Social media has the potential to
increase the non-linear characteristics of interaction (Tadic et al., 2013). Due to nonlinearity, the
direction, velocity and intensity of avalanche/groundswell originated by negative experience is
Possibility to anonymity
Many social media sites allow anonymous “freedom of speech”. Naturally this freedom can be used both
for good or bad. Yun & Park (2011) and Verhagen et al. (2013) have identified different consequence of
online anonymity. According to them anonymity makes people more honest in sharing their negative
experiences online. This is because Internet prevents people from facing any social consequences. Derks
et al. (2004), for example, have suggested that anonymity creates deindividuation and may lead to
antinormative behaviour. Anonymity is considered a major factor of “disinhibitive behaviour” (Lapidot-
Lefler & Barak, 2012). Lapidot-Lefler & Barak (2012) have further argued that anonymity may cause
social media users “to feel unaccountable for their negative actions, as they cannot behave identified as
the perpetrators of certain actions or behaviors”. This provokes toxic behaviour such as impulsive and
aggressive cyber-bullying and off-topic and off-color comments (Kietzmann et al., 2011). Seemingly, the
possibility to anonymity, whether in good or bad purpose, tempts people to express negative emotions in
social media.
Key complainers
All users are not equal in terms of their influence on diffusion of negative emotions online. Adapting
Russell’s circumplex model it seems quite self-evident that individuals who have emotionally negative
valence and positive arousal have different role than those who have negative valence and negative
arousal. Annoyed individual is probable more keen to ventilate in social media than disappointed one.
Users, who are biased to provide negative feedback in social media, can be labelled in many ways. Noble
et al. (2012), for example, have labelled them as “trolls”, “rager”, “misguided” and “unhappy customer”.
Although there are differences in behaviour between the labelled users, what they share is that they are
able to create non-linearly developing viral events that spread more widely and quickly than expected (cf.
Hemsley & Mason, 2013). Worth noting is that in recent years, many bloggers and twitterists have won a
superior audience size compared with traditional mediums (Sandes et al., 2013). It is expected that this
creates a fertile ground for emotional bursts.
Emergent result
Collective emotions result from the process where each individually continually decides with which other
actors it will engage, and what emotion it will share with them. Collective emotion is an emergent whole,
which display properties which cannot be traced back to individual contributions (Schweitzer & Garcia,
2010). This is what happened in the chain of events called “Arab Spring”. Arab Spring was a protest
movement which was initiated in Tunisia by a Facebook campaign run by the opposition “April 6 Youth
Movement” (Stepanova, 2011). The movement generated tens of thousands of positive responses to the
call to rally against government policies (Stepanova, 2011). During the movement social media carried
inspiring stories of protests. Mass forms of socio-political protest facilitated by social media networks
represent emergent behaviour as there is no possibility to pinpoint any specific event or act, which is
accountable for the rising local activities into regional, and in some extent even to global protest. The
power of “Arab Spring” rests on the movement’s ability to inspire disappointed and frustrated people into
collective action. Inflow of negative emotions leads to the emergence of patterns of themes which no
individual could have decided. Seemingly, things just happen without one particular reason. Collective
negative emotion, differs from individual negative emotion in terms of quantity and quality
The anatomy of the diffusion of negative emotion in social media is summarized in Figure 3.
Figure 3. The anatomy of collective negative emotion in social media.
From the organization’s perspective, negative emotional experiences are more dangerous than positive
one because they pose a threat to existence of organisation. Studies indicate that negative eWOM may
have very strong effects on organisations’ performance. Wangeheim (2005), Chevalier & Mayzlin (2006)
and Park & Lee (2009), among others, have identified that negative evaluations of products and services
have a stronger effect than positive ones. Negative eWOM affects negatively on brand image (Jansen et
al., 2009), consumers preferences (Khare et al., 2011) and purchase decisions (Fagerstrom & Ghinea,
2011). One possible explanation for this is that negative eWOM is more diagnostic than positive eWOM.
However, negative emotional experiences can also be valuable and useful for organisation. They can be
turned positive one. This is at least for two reasons. Firstly, as suggested before, for individual venting
negative emotions may act as catharsis helping people to feel calm and relaxed. For organisation this
offers an opportunity to engage emotionally with venting individuals. Many studies have found that
empathetic complaint management not only solve the problem but also strengthen customer relationship
(Estelami, 2000; Kirkby et al., 2001, Lee & Hu, 2004). A bit paradoxically, it has been found that
“customers rate service performance higher if a failure occurs and the contact personnel successfully
addresses the problem than if the service had been delivered correctly the first place (Hoffman & Bateson,
2001; Lee & Hu, 2004). Secondly, negative emotions may reveal unmet customer needs and preferences.
Von Hippel (2005), for example, has suggested that users of products and services themselves know the
best how the products and services actually meet their needs and how products and services should be
improved. Social media heralds of collaborative organisation in which employees, customers and other
stakeholders create spontaneously micro-scale innovation networks which can be exploited for solving
many resource problems (Morgan, 2012). Ignoring social network effects in the design process leads to a
substantially inferior product design (Gunnec & Raghavan, 2013) and to negative customer experience.
As social media also enables anonymity, which, in turn makes people more honest (e.g. Verhagen et al.,
2013), it is therefore reasonable to claim that social media is powerful tool for turning negative
experiences and emotions into positive ones.
This paper has proposed the anatomy of the diffusion of negative emotions in social media. The diffusion
of negative emotion is defined as a complex process. It is a process in which actors affect others and are
being affected by others. It is suggested that social media increase the odds that individually felt negative
emotions escalate into collective negative emotions. Individual negative emotions are inclined to
clusterize. As social media has removed time delays and physical distances, what has happened in local
level can become a global issue and other way round. Social media allows globallocal interplay in
venting negative emotions. Possibility to post negative information anonymously and the role of key
complainers enable non-linear dynamics. This may create emergent whole which cannot be traced back to
individual emotions. This paper does not contain any empirical data. Naturally, in order to validate the
anatomy of the diffusion of negative emotions, empirical research is needed.
This paper has not focused on any particular social media platforms. However, the findings of studies
which have compared different social media sites (Kietzmann et al., 2011; Hughes et al., 2012) implicate
that social media sites differ from each other based on their capacity to convey negative emotions. It can
be hypothesized, for example, that social media sites that allow anonymous posts differ on sentiment from
those sites which require identification. It has been proposed that Twitter offers greater user anonymity
than Facebook, which, in turn, may mean that Twitter provoke more “toxic” behavior (cf. Lapidot-Lefler
& Barak, 2012; Hughes et al., 2012). Furthermore, it can be supposed that mobile use of social media
potentially increase negative emotional bursts (cf. Kwon et al., 2013). This is because pocket carried
devices smart phones and tablets with wide range of applications enable almost real-time reaction, for
example, to bad customer service. This paper has not touched the economic consequences of negative
emotions, but on the basis of research done in negative eWOM, it can be, however, suggested that
organisation’s ability to detect negative sentiments related their products, services, brand images or
businesses becomes more and more importantly. This argument is in line with Rintamäki et al. (2007),
among others, who have identified that emotions play critical role in the competitive customer value
The paper implicitly suggests that in order to handle negative emotions shared in social media, the
organization should aim at the ability to map the seeds of negative avalanches/groundswells as early as
possible. This is because the value of negative emotion is the function of time. One possible approach to
increase organisations’ ability to detect emotional weak signals is taking advantage of sentiment analysis
(Liu, 2010; Thelwall & Buckley, 2013). Sentiment analysis refers herein to computational study of
sentiments, affects and emotions expressed in social media texts. Sentiment analysis is based on the very
simple idea i.e. texts are subjective which may express some personal feeling, view, emotion, or belief.
Although, a completely automated solution is nowhere in sight (Lie, 2010), it is expected that sentiment
analysis provides useful tool organisations to improve their ability to detect symptoms of collective
negative emotions before they become an issue.
Bar-Tal, D., Halperin, E. & De Rivera, J. (2007) ”Collective emotions in conflict situations: Societal
implications”, Journal of Social Issues, 63(2), 441460.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001) ”Bad is stronger than good”, Review of
General Psychology, 5, 322370.
Bae, Y. & Lee, H. (2012) ”Sentiment analysis of twitter audiences: Measuring the positive or negative influence of
popular twitterers”, Journal of the American Society for Information Science & Technology, 63(12), 25212535.
Bernoff, J. and Li, C. (2008) “Harnessing the Power of the Oh-So-Social Web”, MIT Sloan Management
Review, Spring 2008, 3642.
Brehm, J. W. (1999) “The intensity of emotion”, Personality and Social Psychology Review, 3(1), 222.
Cacioppo, J. T. & Gardner, W. L. (1999) “Emotion”, Annual Review of Psychology, 50, 191214.
Chevalier, J. & Mayzlin, D. (2006) “The effect of word of mouth on sales: Online book reviews”, Journal of
Marketing Research, 63, 345354.
Chmiel, A., Sienkiewicz, J., Thelwall, M., Paltoglou, G., Buckley, K., Kappas, A. & Hołyst, J. A. (2011) “Collective
Emotions Online and Their Influence on Community Life”, PLoS ONE, 6(7), 18.
Denyer, D., Parry, E. and Flowers, P. (2011) “Social”, “Open” and “Participative”? Exploring
Personal Experiences and Organizational Effects of Enterprise 2.0 Use”, Long Range Planning,
44, 375396.
Estelami, H. (2000) “Competitive and procedural determinant of delight and disappointment in consumer complaint
outcomes”, Journal of Service Research, 2(3), 285300.
Fagerstrom, A. & Ghinea, B. (2011) “On the motivating impact of price and online recommendations at the point of
online purchase”, International Journal of Information Management, 31, 103110.
Gilbert, E. & Karahalios, K. (2010) “Widespread worry and the stock market”, a paper presented at the 4th
International AAAI Conference on Weblogs and Social Media (ICWSM), Washington, DC, USA.
Gunnec, D. & Raghavan, S. (2013) “Integrating social network effects in the share-of-choice problem”,
Martin, R. C., “Anger on the Internet: The Perceived Value of Rant-Sites” By: Martin, Ryan C.; Coyier, Kelsey
Ryan; VanSistine, Leah M.; Schroeder, Kelly L. CyberPsychology, Behavior & Social Networking. Feb2013, Vol. 16
Issue 2, 119-122.
Hemsley, J. and Mason, R. M. (2013) “Knowledge and Knowledge Management in the Social Media Age”, Journal
of Organizational Computing and Electronic Commerce, 23(1-2), 138167.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G. & Gremler, D. D. (2004) “Electronic word of mouth via consumer-
opinion platforms: What motivates consumers to articulate themselves on the Internet?“, Journal of Interactive
Marketing, 18, 3852.
Herr, P. M., Kardes, F. R. & Kim, J. (1991) “Effects of word-of-mouth and product attribute informationd on
persuasion: An accessibility diagnosticity perspective“, Journal of Consumer Research, 17, 454462.
von Hippel, E. (2005) Democratizing Innovation, The MIT Press, Cambridge.
Hoffman, D. L. & Bateson, J. (2001) Essentials of services marketing: Concepts, strategies, and cases, Mason, OH.
Hughes, D. J., Rowe, M., Batey, M. & Lee, A. (2012), “A tale of two sites: Twitter vs. Facebook and the personality
predictors of social media usage”, Computers in Human Behavior, 28(2), 561569.
Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) ”Twitter power: Tweets as electronic word of mouth”,
Journal of the American Society for Information Science and Technology, 60(11), 21692188.
Jones, S. A., Aiken, D. K. & Boush, D. M. (2009) “Integrating Experience, Advertising, and Electronic Word of
Mouth”, Journal of Internet Commerce, 8(3/4), 246267.
Kaplan, A. M. & Haenlein, M. (2010) “Users of the world, unite! The challenges and opportunities of social media”,
Business Horizons, 53, 5968.
Khare, A., Labrecque, I. I. & Asare, A. K. (2011) “The assimilative and contrastive effects of Word-of-mouth
volumen: An experimental examination of online consumer ratings”, Journal of Retailing, 87(1), 111126.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P. and Silvestere, B. S. (2011), “Social media? Get serious!
Understanding the functional blocks of social media”, Business Horizons, 54(3), 241251.
Kirkby, J. Thompson, E. & Wecksell, J. (2001) Customer experience: The voice of the customer, Research note No.
TG-14-9567, Stamford, CT: Gartner, Inc.
Kwon, O., Kim, C-R. &Kim, G. (2013) ”Factors affecting the intensity of emotional expressions in mobile
communications”, Online Information Review, 37(1), 114131.
Lapidot-Lefler, N. & Barak, A. (2012) “Effects of anonymity, invisibility and lack of eye-contact on toxic online
disinhibition”, Computers in Human Behavior, 28(2), 434443.
Lee, S. & Cude, B. J. (2012) “Consumer complaint cannel choice in online and off-line purchases”, International
Journal of Consumer Studies, 36, 9096.
Lee, C. C. & Hu, C, (2004) ”Analyzing Hotel Customers' E -- Complaints from an Internet Complaint Forum”,
Journal of Travel & Tourism Marketing, 17(2/3), 167181.
Lewin, K. (1943)” Psychology and the process of group living. Bull SPSSL”, Journal of Social Psychology, 17, 113
Li, C. & Bernoff, J. (2011) Groundswell, Harvard Business Review Press.
Litvin, S. W., Goldsmith, R. E. & Pana, B. (2008) “Electronic word-of-mouth in hospitality and tourism
management”, Tourism Management, 29, 458468.
Liu, B. (2010) “Sentiment Analysis and Subjectivity”, in Indurkhya, N. & Damerau, F. J. (eds.) Handbook of Natural
Language Processing, Chapman&Hall/CRC Press, Boca Raton, Florida.
Mangold, W. G. & Faulds, D. J. (2012) “Social media: The new hybrid element of the promotion mix”, Business
Horizons, 52(4), 358365.
Martin, R. C., Coyier, K. R., VanSistine, L. M. & Schroeder, K. L. (2013) “Anger on the Internet: The Perceived
Value of Rant-Sites”, CyberPsychology, Behavior & Social Networking, 16(2), 119122.
Meadows- Klue, D. (2008)” Falling in Love 2.0: Relationship marketing for the Facebook
generation”, Journal of Direct, Data and Digital Marketing Practice, 9 (3), 245250.
Morgan, J. (2012) The Collaborative Organization: A Strategic Guide to Solving Your Internal
Business Challenges Using Emerging Social and Collaborative Tools, McGraw-Hill, New York.
Noble, C. H., Noble., S. M. & Adjei, M. T. (2012) “Let them talk! Managing primary and extended online brand
communities for success”, Business Horizons, 55, 475483.
Park, C. & Lee, T. M. (2009) ”Information direction, website reputation and eWOM effect: A moderating role of
product type”, Journal of Business Research, 62(1), 6167.
Parra-López, E., Bulchand-Gidumal, J., Gutiérrez-Tano, D. & Diaz-Armas, R. (2011) “Intentions to use social media
in organizing and taking vacation trips”, Computers in Human Behavior, 27, 640654.
Rintamäki, T., Kuusela, H. & Mitronen, L. (2007) “Identifying competitive customer value propositions in retailing”,
Managing Service Quality, 17(6), 621634.
Robertson, S. P., Douglas, S., Maruyama, M. & Semaan, B. (2013) ”Political discourse on social networking sites:
Sentiment, in-group/out-group orientation and rationality”, Information Polity: The International Journal of
Government & Democracy in the Information Age, 18(2), 107126.
Rogers, E. M. (2003) Diffusion of innovations (5th ed.), Free Press, New York.
Rozin, P., & Royzman, E. B. (2001) „Negativity bias, negativity dominance, and contagion”, Personality and Social
Psychology Review, 5, 296320.
Rubin, A. (2011) “Living in the age of emotional rationality: Wendell Bell, social media
and the challenges of value change”, Futures, 43, 583589.
Russell, J. A. (1980) “A circumplex model of affect”, Journal of Personality and Social Psychology, 39(6), 1161
Sandes, F. S., & Urdan, A. T. (2013) “Electronic Word-of-Mouth Impacts on Consumer Behavior: Exploratory and
Experimental Studies”, Journal of International Consumer Marketing, 25(3), 181197.
Sawhney, M. S. & Kotler, P. (2001) ”The age of information democracy”, in Iacobucci, D. (eds.) Kellog on
Marketing, 386408, John Wiley and Sons, New York, NY.
Schweitzer, F. & Garcia, D. (2010) “An agent-based model of collective emotions in online communities”, European
Physical Journal B -- Condensed Matter, 77(4), 533545.
Stepanova, E. (2011) “The role information communication technologies in the Arab Spring – Implications beyond
the regions”, PONARS Eurasia Policy Memo No. 159, Institute of World Economy and International Relations,
Russian Academy of Sciences.
Tadić, B., Gligorijević, V., Mitrović, M., & Šuvakov, M. (2013) ”Co-Evolutionary Mechanisms of Emotional Bursts
in Online Social Dynamics and Networks”, Entropy, 15(12), 50845120.
Tapscott, D. & Williams, A. D. (2007) Wikinomics: How Mass Collaboration Changes Everything,
Portfolio/Penguinn, Toronto, ON.
Thelwall, M. & Buckley, K. (2013) “Topic-Based Sentiment Analysis for the Social Web: The Role of Mood and
Issue-Related Words”, Journal of the American Society for Information Society and Technology, 64(8), 16081617.
Thelwall, M., Wilkinson, D., & Sukhvinder, U. (2010) “Data mining emotion in social network communication:
Gender differences in MySpace”, Journal of the American Society for Information Science and Technology, 61(1),
Thogersen, J., Juhl, J. J. & Poulsen, C. S. (2009) ”Complaining: A funtion of attitude, personality, and situation”,
Psychology & Marketing, 26(8), 760777.
Verhagen, T., Nauta, A. & Feldberg, F. (2013). Negative online word-of-mouth: Behavioral indicator or emotional
release? Computers in Human Behavior, 29, 14301440.
Vuori, V. (2011) Social Media Changing the Competitive Intelligence Process: Elicitation of Employees’
Competitive Knowledge, Publication 1001, Tampere University of Technology, Tampere.
Wangenheim, F. V. (2005) “Postswitching negative word of mouth”, Journal of Service Research, 8(1), 6778.
Yun, G. W. & Park, S.-Y. (2011) ”Selective posting: Willingness to post a message online”, Journal of Computer-
Mediated Communication, 16, 201277.
Zaugg, A. & Jäggi, N. (2006) ”The impact of customer loyalty on complaining behavior”, in Isaias, P., Baptista
Nunes, M. & Martinez, I. J. (eds.) IADIS International Conference WWW/Internet, 119123, Murcia, Spain.
... The following sections highlight some investigations focusing on associating emotions and social networks especially in Facebook and Twitter. Researchers have put in a lot of effort in realizing the dispersion of emotion through the content shared in social networks [8,9,10,11,12,13,14,15] Also, it is noted that most of the emotions are exchanged through Facebook and Twitter activities. ...
... As a popular social network, Facebook provides a good way of emotion sharing through different activities and contents [12,13,14,16,17,18]. According to the researchers, emotion is dispersed in Facebook in the form of topics, messages, wall content, size, and density of the connectivity of individuals. ...
... Similarly, Twitter has been serving as a good emotion diffuser through tweets [8,14,19,20,21]. According to these researchers, content, topic, messages, type of conversation, social ties, amount of network activity of individuals, participatory patterns, and language use act as emotion carriers in Twitter. ...
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Personalization is the process of customizing social network pages of users according to their needs and personal interests. It can also be used for filtering unwanted information from an individual's page received from other users, in case this information is unpleasant or unacceptable. To avoid unwanted information from a particular user in current social networks, the user needs to be denied accessibility by blocking them. However, instead of blocking the user, it would be preferable to have a mechanism that prevents the undesirable content in a user's social network page. Thus, this paper presents a model that determine the emotions shared in the content of a social network page by the user. The model determines the dominant emotions for a period of time and uses these to filter the content using the user's dominant emotions. Using the developed model, a novel system based on item based collaborative filtering process to personalize the user's social network page has been developed. A user study involving 5000 Twitter messages shows that the developed system performs satisfactory with a correctness in the filtering process of 87%. © 2018, University of Zagreb, Faculty of Organization and Informatics. All rights reserved.
... It has shown through studies that Facebook has a high influence on people's mental health [2]. Moreover, SNS is an open area for many people to express their negative emotions [3]. Most of the mental illnesses can be identified at the initial stage itself based on their SNS activities. ...
... As a basic feature set, most of the researches used linguistic features such as n-grams, word length, emoticons and abbreviation, and punctuation [1], [3]. When analysing personal activities there are nineteen dimensions in Linguistic Inquiry and Word Count (LIWC) approach [1]. ...
... The prevalence of negative stances on social media is a recognized phenomenon [32][33][34]. Our algorithm made it possible to identify not only the final positive or negative stance of statements but also the degree of intensity of negative or positive statements in these groups, which is a «cold» demographic temperature of social groups. ...
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Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks.
... However, in addition to the traditional and more understood deviances, it is also quite likely that the employee may attempt to malign the organizational image outside the workplace. For example, showing the employer in bad light in social media may be considered as an expression of vengeance against perceived stress and could take forms such as venting and smearing (Jalonen, 2014;Wendorf & Yang, 2015;Workman, 2012). Indulgence in counterproductive behaviors, both offline and online, is again likely to be an outcome of loss of job resources along with the inability to replace the same with personal and social resources (Hobfoll, 1989(Hobfoll, , 2001. ...
Disengagement at work is proving to be a source of continued trouble for business organizations. Various estimates suggest that in excess of 70% of the workforce is either passively or actively disengaged, which in turn subjects the organizations to enormous financial burden. Regretfully, this problem has not found sufficient intellectual resonance in the academia. Therefore, employing conservation of resources (COR; Hobfoll, 1989) as the guiding theory, in this research, we conduct an integrative literature review to consolidate the extant approaches to disengagement at work. Apart from explaining the construct, we also identified its antecedents, moderating influences, and outcomes. Implications for human resource development (HRD) research and practice have been discussed. We believe that such an attempt is likely to encourage an informed debate on the subject in the academic domain, while helping practitioners identify actionable interventions.
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Saat ini pengguna media sosial tidak terbatas pada dewasa dan remaja, tetapi sudah banyak digunakan oleh anak-anak. Melalui media sosial mereka cenderung melakukan perbandingan sosial. Tujuan dari penelitian ini untuk menguji hubungan antara penggunaan media sosial dan perilaku perbandingan sosial pada fase anak-anak akhir. Penelitian ini menggunakan desain penelitian korelasional dengan teknik pengambilan sampel non random sampling. Responden yang diteliti sebanyak 101 subyek dengan karakteristik siswa kelas VI SD, bisa berbahasa Indonesia, dan memiliki akun media sosial. Peneliti mengambil sampel satu Sekolah Dasar di Surabaya. Instrumen yang digunakan adalah skala perbandingan sosial yang disusun Gibbons & Buunk (1999) berdasarkan teori perbandingan sosial menurut Festinger (1945) dan diadaptasi oleh (Putra, 2017) serta daftar aktivitas penggunaan media sosial oleh Rosen (2013). Berdasarkan hasil penelitian, diketahui bahwa koefisien korelasi yang didapat antara skor total penggunaan media sosial dengan skor total perbandingan sosial adalah sebesar 0.313 dan signifikan pada Los 0,01 (nilai p = 0,001< 0.01), yang berarti terdapat korelasi positif yang signifikan antara penggunaan media sosial dengan tingkat perbandingan sosial. Semakin tinggi skor total penggunaan media sosial maka skor tingkat perbandingan sosial akan semakin tinggi, begitu juga sebaliknya. Peran orang tua sangat dibutuhkan dalam proses pendampingan anak usia akhir agar tidak terbawa dampak negatif dalam perbandingan diri melalui media sosial.
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People have been using different platforms to discuss some of the world's most serious issues and problems. In this age of information, the social media is now one of the primary mediums used to convey information or messages. People nowadays have been communicating their thoughts through social media memes, normally, either to downplay issues or just to indirectly voice out what is in their mind. These memes also serve as a mode of starting conversation. This paper analyzed how internet memes has been used by Filipino netizens to achieve a particular purpose while discussing or presenting the issues or problems through memes based on their perspective. Using Social Convergence Theory (SCT) or the fantasy theme analysis, this study found out the unique rhetorical visions or shared fantasies formed in these memes. Then, using the Invitational Rhetoric Theory (IRT), the study found out that the analyzed memes mostly served different purposes and did not created an environment that encourages audiences to share their perspective. The study concluded that if a meme has secured and created an environment that encourages audiences to share their perspective, there would be a considerable amount of engagement it will garner. Keywords: Internet Memes, Fantasy Themes Analysis, Invitational Rhetoric Theory.
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ABSTRAK Era Big Data dan perlombongan data telah mewarnai dunia penyelidikan data teks yang dijana pengguna. Peningkatan pengguna media sosial setiap tahun bermaksud pertambahan data dan maklumat yang dijana pengguna memenuhi ruang pelayan di kerangka utama laman sesawang terlibat. Data dan maklumat ini amat bernilai sekiranya digunakan untuk tujuan penyelidikan. Namun begitu, bagaimanakah untuk mengekstrak bilangan data teks dalam jumlah yang banyak dengan mudah? Terdapat alat-alat pengesktrakan data teks yang telah dicipta untuk menyelesaikan masalah ini. Malah, banyak kajian terdahulu yang menggunakan data teks sebagai data utama dalam kajiannya tetapi tiada penerangan jelas tentang cara menggunakan alat pengekstrakan data teks tersebut. Oleh itu, kajian ini membincangkan berkenaan lima alat pengesktrakan data teks, ciri-ciri alat pengekstrakan data teks dan perbandingan terhadap 5 alat tersebut. Kajian ini telah melalui fasa penerokaan penting iaitu pemasangan perisian, pengujianan dan hasil output bagi setiap alat tersebut. Hasil kajian ini mendapati bahawa, setiap penggunaan alat pengekstrakan data teks mempunyai ciri istimewa yang tersendiri iaitu jenis perisian, tahap penggunaan, asas pengetahuan pengguna dan jenis laman sesawang yang boleh diekstrak. Selepas melalui proses pengujian bagi setiap alat, kajian ini mendapati bahawa alat pengekstrakan data teks ini sangat memudahkan pengguna untuk mendapatkan data teks dalam kuantiti yang banyak secara sistematik. Oleh itu, semoga kajian ini dimanfaatkan sepenuhnya bagi membolehkan para penyelidik berinteraksi dan berkongsi idea dengan ramai orang serta menjadi rujukan untuk penyelidikan akan datang. ABSTRACT The era of Big Data and Data Mining has colored the world of user-generated content research in text data. The rising number of social media users each year results in the expansion of data and information generated by users whom filling-up the server space on the main homepage. This
The utilization of Social Networking Sites (SNS) like Twitter is expanding quickly and particularly by the more youthful age. The profit capacity of SNS enables us to express their interests, emotions and offer their day by day schedule. SNS sites such as Twitter allow for constant investigation of user behaviour. Such examples are important for the psychological research network to comprehend the periods and area of most prominent interest. Worlds fourth biggest disease depression has turned out to be a standout amongst the most huge research subject. We propose a system which uses tweets as source of data and SentiStrength sentiment analysis to create a training data for our system and a Back Propagation Neural Network (BPNN) model to classify the given tweets into depressed or not depressed categories.
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Due to the growth of the Internet as a social media and channel of communication among consumers, electronic word of mouth (eWOM) has gained strength and attention from marketing researchers. This article aims to verify if eWOM affects consumer behavior and the possibility companies may have of managing eWOM by actively responding to comments posted by consumers. An exploratory and experimental study showed that exposure to comment (both negative and positive) impacts brand image. Negative-feedback management reduces the impact on brand image but did not change the impact on purchase intention.
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events, and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, or feelings toward entities, events, and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on themining and retrieval of factual information, e.g., information retrieval (IR), Web search, text classification, text clustering, and many other text mining and natural language processing tasks. Littleworkhadbeendone on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.
Competitive intelligence process aims to provide actionable information about the external business environment to back up decision-making in companies. The affects that the rise of social media may have on competitive intelligence is a topic of interest to both practice and theory. The main objectives of this dissertation are to understand how social media changes the competitive intelligence process and how can it enhance the elicitation of employees’ competitive knowledge. The research questions are studied using both theoretical and empirical research approach. Empirical study consists of three data sets complementing each other, adopting several methods and perspectives. The results of the dissertation suggest that social media has an effect on companies’ information environment, as the widespread use of social media produces more volume and more versatile information than before. In the competitive intelligence context this influences information gathering especially: social media for its part increases the available information sources, but it also offers technologies to automate some parts of information gathering and processing. In addition, use of suitable social media tools can have affects on the elicitation of employees’ competitive knowledge and making competitive knowledge more visible in a company. Social media provides an opportunity to implement the competitive intelligence process as participative and collaborative and engaging employees in the process. The role of the employees shifts to that of more active participants shaping the collaborative understanding by contributing their competitive knowledge to the process as well as better benefiting more from others’ competitive knowledge. However, the success of using social media in better utilising and sharing employees’ competitive knowledge relies heavily on utility, perceived usefulness and affordance of the tools as well as how motivated the employees are to use it for knowledge sharing. The main motivating factors and barriers are in line with those regarding general knowledge sharing. The main contributions include increasing knowledge on the connection between social media and competitive intelligence: how the emergence of social media affects carrying out the competitive intelligence process and especially sharing of employees’ competitive knowledge. In addition, the research reveals the motivational factors and barriers related to employees’ willingness to use social media for sharing competitive knowledge. The findings also have practical managerial implications for companies planning to adopt social media for competitive knowledge sharing, as they provide means for them to prepare the conditions for successful utilisation and active employee participation.
Accounting for social network effects in marketing strategies has become an important issue. Taking a step back, we seek to incorporate and analyze social network effects on new product development and then propose a model to engineer product diffusion over a social network. We build upon the share-of-choice (SOC) problem, which is a strategic combinatorial optimization problem used commonly as one of the methods to analyze conjoint analysis data by marketers in order to identify a product with largest market share, and show how to incorporate social network effects in the SOC problem. We construct a genetic algorithm to solve this computationally challenging (NP-Hard) problem and show that ignoring social network effects in the design phase results in a significantly lower market share for a product. In this setting, we introduce the secondary operational problem of determining the least expensive way of influencing individuals and strengthening product diffusion over a social network. This secondary problem is of independent interest, as it addresses contagion models and the issue of intervening in diffusion over a social network, which are of significant interest in marketing and epidemiological settings.
A theory is outlined that assumes that emotions are motivational states with the special function of producing adaptation to situational conditions. The theory assumes that the emotional system lies in the central nervous system, that it is fast to react, able to change quickly from one emotional state to another, produces only one emotion at a time, and that the intensity of that emotion is a nonmonotonic function of deterrence to the aim of the emotion. Supporting data from several experimental tests are reported, and selected theoretical problems are discussed.
Service failure is unavoidable. However, depending on the type of loyalty, customers react differently to critical incidents causing dissatisfaction, e.g. truly loyal customers are less inclined to end the relationship with their provider. Intending to stay, they will instead complain to the company. The type of customer loyalty also influences the channel choice for com-municating dissatisfaction. As online complaints require more trust in the company, e-communication is more likely to be chosen by truly loyal customers. This paper provides a conceptual framework demonstrating the effects of customer loy-alty on complaint response patterns and channel choice using the example of the complaint response "voice company".
Twitter is a popular microblogging service that is used to read and write millions of short messages on any topic within a 140-character limit. Popular or influential users tweet their status and are retweeted, mentioned, or replied to by their audience. Sentiment analysis of the tweets by popular users and their audience reveals whether the audience is favorable to popular users. We analyzed over 3,000,000 tweets mentioning or replying to the 13 most influential users to determine audience sentiment. Twitter messages reflect the landscape of sentiment toward its most popular users. We used the sentiment analysis technique as a valid popularity indicator or measure. First, we distinguished between the positive and negative audiences of popular users. Second, we found that the sentiments expressed in the tweets by popular users influenced the sentiment of their audience. Third, from the above two findings we developed a positive-negative measure for this influence. Finally, using a Granger causality analysis, we found that the time-series-based positive-negative sentiment change of the audience was related to the real-world sentiment landscape of popular users. We believe that the positive-negative influence measure between popular users and their audience provides new insights into the influence of a user and is related to the real world.
Purpose ‐ The use of text-based communications such as instant messaging or social media such as Twitter has been growing significantly as the use of mobile devices increases. Not only do people share information via mobile communication, there are significant implications for advertising and marketing. Due to display limitations, however, the message senders use various conventions in addition to the text-based message to more clearly and richly express emotions. Since users use a range of expressions to convey these emotions, it would be very useful to verify the relationships between users' emotional expressions and receivers' perceptions of the expressions. The purpose of this paper is to propose an integrated model to examine the relationship between emotional expressions and the emotional intensity of the receivers. Design/methodology/approach ‐ The authors formulated a series of research hypotheses and tested them using empirical survey data. The research model used is based on regression analysis with dummy variables for statistical analyses. Findings ‐ First, emotional intensity had a closer relationship to user acceptance than was expected. Second, the use of exclamation marks and emotional messages are far less acceptable in negative messages. Third, the high formalisation group has a more positive emotional intensity in their basic expression. Originality/value ‐ The authors successfully determined that emotional expressions significantly affect the message receivers' emotional intensity and hence acceptance of the message.