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Abstract

The present study experimentally examines the impact of emoticons on message interpretation among secondary school students (N = 105). Furthermore, perceived motives for emoticon use are examined. Results show that emoticons do have an impact on message interpretation. Emoticons are useful in strengthening the intensity of a verbal message. Furthermore, it is possible to create ambiguity and express sarcasm online by varying the valence of the emoticon and the valence of the message. Overall, the authors conclude that to a large extent, emoticons serve the same functions as actual nonverbal behavior.
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Social Science Computer Review
DOI: 10.1177/0894439307311611
2008; 26; 379 originally published online Dec 10, 2007; Social Science Computer Review
Daantje Derks, Arjan E. R. Bos and Jasper von Grumbkow
Emoticons and Online Message Interpretation
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379
Social Science Computer Review
Volume 26 Number 3
August 2008 379-388
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Emoticons and Online
Message Interpretation
Daantje Derks
Open University of the Netherlands
Arjan E. R. Bos
Erasmus University Rotterdam
Jasper von Grumbkow
Open University of the Netherlands
The present study experimentally examines the impact of emoticons on message interpretation
among secondary school students (N = 105). Furthermore, perceived motives for emoticon use
are examined. Results show that emoticons do have an impact on message interpretation.
Emoticons are useful in strengthening the intensity of a verbal message. Furthermore, it is pos-
sible to create ambiguity and express sarcasm online by varying the valence of the emoticon
and the valence of the message. Overall, the authors conclude that to a large extent, emoticons
serve the same functions as actual nonverbal behavior.
Keywords: emoticon; computer-mediated communication; message interpretation; motives
T
oday, a very large amount of our daily interaction with others takes place on the
Internet. A specific characteristic of computer-mediated communication (CMC) is that
it is largely text-based, which automatically implicates that there is a lack of nonverbal
cues. Besides the verbal part of a message, one way to give expression to our thoughts is
by using emoticons. The present study investigates perceived motives for emoticon use and
examines the impact of emoticons on the interpretation of e-mail messages.
Emoticons in CMC
Emoticons resemble facial nonverbal behavior and may serve at least some of the same
functions as nonverbal behavior in face-to-face (F2F) communication (e.g., Derks, Bos, &
von Grumbkow, 2007). The basic functions of nonverbal cues in F2F communication are
providing information, regulating social interaction, and expressing intimacy (e.g., Ekman
& Friesen, 1969). Nonverbal cues in F2F communication may intensify or tone down the
emotional expression (Lee & Wagner, 2002).
In CMC, there is a lack of visual cues, which implies that not all information is fully
transferred (McKenna & Bargh, 2000). As a consequence, e-mail messages can be misin-
terpreted, especially when the writer is trying to be funny (Sanderson, 1993). Writing down
emotional messages changes the intensity of the emotion, because there is time to read over
the text and reflect on one’s emotional state (Fischer, in press). Emoticons may enhance the
exchange of emotional information by providing additional social cues beyond those found
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in the message text (Thompson & Foulger, 1996). They are used to augment the meaning
of a message (Rezabek & Cochenour, 1998). Emoticons may be used to emphasize or clar-
ify one’s feelings but also to soften a negative tone and to regulate the interaction, just as
smiles and frowns do in daily life.
Perceived Motives for Emoticon Use
The lack of nonverbal cues and the use of emoticons have some advantages over regular
F2F communication. There is no risk of unintentionally leaking nonverbal information,
which makes the Internet a relatively “safe” environment for communication (McKenna,
Green, & Gleason, 2002). Emoticons are used more consciously than actual nonverbal
behavior, which implies that there is more control over the message a person wants to con-
vey. As a consequence, it might be easier to regulate emotions. Different functions have
been ascribed to facial displays in F2F communication through the years. The “emotional”
view states that facial displays are a result of a person’s internal emotional state (e.g.,
Ekman, 1972). According to Fridlund’s (1994) “behavioral ecology” view, facial displays
are social signals communicating social motives. More recent evidence shows that facial
displays are affected by both emotional and social factors (Hess, Banse, & Kappas, 1995;
Jakobs, Manstead, & Fischer, 1999). As emoticons can be seen as nonverbal surrogates
resembling facial expressions, it is plausible that CMC users also have social motives for
adding emoticons to their messages. In a previous study (Derks, Bos, & von Grumbkow, in
press), the motives for using emoticons were examined. Overall, emoticons were most used
for the expression of emotion, for strengthening the verbal part of the message (with a sup-
porting emoticon), and for expressing humor. This is in line with the approach that facial
displays are affected by social factors as well as emotions (Hess et al., 1995; Jakobs et al.,
1999). However, the lack of nonverbal cues in CMC also has consequences for the decod-
ing and interpretation of a message by the receiver. Because there is no facial feedback, the
writer is uncertain whether the receiver will interpret the message exactly how he or she
intended it. Therefore, in the current study, we examine how receivers perceive the motives
of the senders of a CMC message for using an emoticon.
Emoticons and Message Interpretation
To our knowledge, Walther and D’Addario (2001) were the first to experimentally test
the impact of emoticons on the interpretation of e-mail messages. Walther and D’Addario
reasoned that emoticons in CMC can provoke similar strong effects that nonverbal cues
have on F2F communication. This means that the impact of emoticons might be as great as
or even greater than that of verbal messages alone on the interpretation of emotions, espe-
cially in the case of mixed messages. The study by Walther and D’Addario comprised a 2
(valence: positive, negative) × 4 (emoticon: smile, frown, wink, blank) between-subject
design. They presented e-mail messages to the participants. Each message contained a brief
discussion about a movie and then a statement about an economics course, which was the
experimental stimulus. This statement was either positive or negative and was accompanied
by one of the three emoticons or left blank in the control condition. Walther and D’Addario
concluded that emoticons had less impact on message interpretation than expected. They
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argued that emoticons can serve the function of complementing verbal messages at best but
not contradicting or enhancing them (Walther & D’Addario, 2001).
However, we do have indications that emoticons serve at least some of the same func-
tions as actual nonverbal behavior in daily life (see Derks et al., 2007; Derks, Fischer, &
Bos, in press). The paradigm used by Walther and D’Addario (2001) might be the reason
for their rather disappointing results. Therefore, in the present study, we adapted Walther
and D’Addario’s paradigm in several ways. We added a neutral condition, we made partic-
ipants the subject of the e-mail communicating an evaluation of their performance, and we
manipulated the type of interaction partner (good friend vs. stranger). The hypotheses are
largely in line with the hypotheses of Walther and D’Addario and will be outlined below.
Furthermore, it is important to note that we restricted the definition of a “pure” message to
a strictly verbal message, without an emoticon.
Hypotheses
Strengthen a message. The intensity of a message may be toned down in strictly text-
based messages. Nonverbal cues can intensify or tone down the emotional expression (Lee
& Wagner, 2002). Emoticons might help to give a message the intensity the sender wants
to express. In F2F communication, nonverbal cues can augment, illustrate, and accentuate
the words they accompany (Burgoon, 1994). Walther and D’Addario (2001) tested these
hypotheses by measuring how much happiness the writer of the message portrayed. Besides
the writer’s state of happiness, we also assessed the positivity of the message. (See
Hypotheses 1A and 1B in Table 1.)
Mixed messages. Mixed feelings, creating a greater ambiguity, might be communicated
using some positive and some negative cues at the same time. This is very likely, because
mixed feelings are very common in social interaction (e.g., Planalp, 1998). Walther and
D’Addario (2001) compared mixed messages with “pure” messages. In our study, we
define a pure message as a strictly verbal message, without an emoticon. We hypothesize
that emoticons can create the same ambiguity as nonverbal cues in F2F interaction (see
Hypotheses 2A and 2B in Table 1). Mixed messages might be more difficult to interpret.
Walther and D’Addario argued that in mixed messages, the valences of verbal and nonver-
bal messages may cancel each other out, resulting in a more neutral interpretation overall
(see Hypotheses 3A and 3B in Table 1). However, these messages may also convey more
sarcasm. Sarcasm might be communicated using positive words but a negative tone or the
other way around (see Hypotheses 3C and 3D in Table 1).
Winks. The wink emoticon is two sided; the smiling aspect suggests positivity, and the
wink connotes an extra dimension of humor or irony. No matter what the valence of a mes-
sage is, a wink implies that the message has an ulterior meaning and therefore might be sar-
castic (Rezabeck & Cochenour, 1998; Walther & D’Addario, 2001). Hypotheses 4A and 4B
are depicted in Table 1.
Drawing on the perspectives on the relationship between verbal and nonverbal cues in
F2F communication, we may expect that emoticons can fulfill at least some of the same
functions as nonverbal cues in message interpretation.
Derks et al. / Emoticons and Online Message Interpretation 381
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Method
Participants
Participants (N = 105) were secondary school students from a school in Heerlen (in the
Netherlands). Participants from the 4th year were recruited. The group consisted of 49 men
and 56 women. The mean age was 15.48 years (SD
=
0.74). They all participated on a vol-
untary basis, and as a reward for participating, lots were drawn for film vouchers.
Procedure
Students participated together in a classroom under the supervision of a teacher.
Participants received a questionnaire, which they filled out individually. Participation took
about 30 min.
Materials and Design
The instruments used in this research are based on the paradigm of Walther and
D’Addario (2001). The first page explained that the study involved the participants’ experi-
ence with e-mail and chatting, and it told them that they were about to read e-mail messages
382 Social Science Computer Review
Table 1
Hypotheses and Main Outcomes of This Study
Hypothesis 1A: A positive verbal message coupled with a smile emoticon conveys greater positivity and
happiness than a positive verbal message alone. Supported.
Hypothesis 1B: A negative verbal message coupled with a frown emoticons conveys greater negativity and
less happiness than a negative verbal message alone. Rejected. .
Hypothesis 2A: A negative verbal message coupled with a smile emoticons is more ambiguous than a
negative verbal message alone or a positive verbal message alone. Supported.
Hypothesis 2B: A positive verbal message coupled with a frown emoticon is more ambiguous than a positive
verbal message alone or a negative verbal message alone. Supported.
Hypothesis 3A: A negative verbal message coupled with a smile emoticon conveys less negativity than a
negative pure message and less positivity than a positive pure message. Supported.
Hypothesis 3B: A positive verbal message coupled with a frown emoticon conveys less positivity than a
positive pure message and less negativity than a negative pure message. Supported.
Hypothesis 3C: A negative verbal message accompanied with a smile conveys greater sarcasm than a positive
or a negative pure message. Supported.
Hypothesis 3D: A positive verbal message accompanied with a frown conveys greater sarcasm than a positive
or a negative pure message. Supported.
Hypothesis 4A: A negative message coupled with a wink conveys less negativity than a negative pure
message. Supported.
Hypothesis 4B: A positive verbal message coupled with a wink conveys less positivity than a positive pure
message. Rejected.
Hypothesis 4C: A negative verbal message accompanied with a wink conveys greater sarcasm than a negative
pure message. Supported.
Hypothesis 4D: A positive verbal message accompanied with a wink conveys greater sarcasm than a negative
pure message. Supported.
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after which they would have to answer a number of questions. Each message contained an
evaluation of the participant’s performance as a presenter in class. At this point, the manip-
ulations were induced. The sender of the message was manipulated (stranger, good friend).
The valence of the messages varied (positive, negative, neutral). The message was comple-
mented with one of the three emoticons (smile, frown, wink) or left blank in the case of the
control condition. This results in a 2 (partner: stranger, good friend) × 3 (valence: positive,
negative, neutral) × 4 (emoticon: smile, frown, wink, blank) within-subjects design.
Conditions were randomly presented to the participants. The questionnaire contained the
dependent variables in this study (see Table 2 for an overview). The questions were largely
in line with those of Walther and D’Addario but were adjusted to our evaluation message.
The next part of the study consisted of a questionnaire assessing participants’ impres-
sions of the sender’s motive to use a certain emoticon. The emoticons used were smile,
frown, and wink. All motives were measured on 7-point scales, with responses ranging
from 1 (totally disagree) to 7 (totally agree). For example, participants were asked to rate
how much they agreed with the following statement: “When the writer of a message uses
the emoticon ‘smile, he/she wants to express his/her emotions. The motives measured for
each emoticon were to express emotion, to strengthen the message, to manipulate the inter-
action partner, to express humor, to put a remark into perspective, to regulate the interac-
tion, and to express irony.
Results
Manipulation Checks
A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA was conducted
on how positively the participants rated the message. The multivariate main effect of
Derks et al. / Emoticons and Online Message Interpretation 383
Table 2
Measures Used in the First Part of the Study
How do you feel after reading this message? (1 = very negative,7= very positive)
How positively do you rate the message? (1 = very negative,7= very positive)
How familiar was the sender of the message? (1 = very unfamiliar,7 = very familiar)
How does the sender evaluate your performance as presenter? (1 = very negative,7= very positive)
How ambiguous was the message? (1 = very unambiguous,7 = very ambiguous)
How serious was the message? (1 = not serious at all,7 = very serious)
How easy was it to understand the message? (1= very easy,7 = very difficult)
How happy was the writer of the message? (1= very sad,7 = very happy)
How sincere was the writer of the message? (1= very insincere,7= very sincere)
On a scale from 1 to 100, with 1 being the least and 100 being the most, how much happiness did the writer
of the message portray?
On a scale from 1 to 100, with 1 being the least and 100 being the most, how much sarcasm did the writer of
the message portray?
On a scale from 1 to 100, with 1 being the least and 100 being the most, how much humor did the writer of
the message portray?
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valence was significant, F(2, 83) = 181.63, p < .001. Participants rated positive messages
more positively (M
=
5.35, SD
=
0.89) and negative messages more negatively (M
=
2.29,
SD
=
0.94); neutral messages were rated neutrally (M
=
4.05, SD
=
0.73). All means dif-
fer significantly at p < .001. A 3 (valence) × 2 (partner) × 4 (emoticon) repeated-measures
ANOVA was conducted on the participants’ rating of how familiar the sender of the mes-
sage was. The multivariate main effect of partner was significant, F(1, 85) = 128.53, p <
.001. In the good friend condition, the interaction partner was rated as more familiar (M
=
5.07, SD
=
1.34) than in the stranger condition (M = 2.52, SD
=
1.23), p < .001. Thus,
our manipulations of valence and partner have been induced successfully.
Strengthen a message. A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures
ANOVA was conducted on how positively the participants rated the message. The interaction
effect between valence and emoticon was found to be significant, F(6, 79) = 12.93, p < .001.
A positive message coupled with a smile was rated more positively (M = 6.30, SD = 1.06)
than a positive pure message alone (M = 5.48, SD = 1.14). However, a negative message
accompanied with a frown (M = 2.02, SD = .97) was rated as negatively as a negative pure
message alone (M = 2.04, SD = .99). Furthermore, the amount of happiness the writer of the
message portrayed was analyzed. The results showed a significant interaction effect between
valence and emoticons, F(6, 77) = 7.07,p< .001. A positive message with a smile (M =
69.84, SD = 23.03) portrayed more happiness than a positive pure message alone (M =
41.85, SD = 27.28). However, a negative message with a frown (M = 11.99, SD = 16.41)
portrayed just as much happiness as a negative message alone (M = 14.23, SD = 18.70).
Mixed messages. A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA
was conducted on how ambiguously the participants rated the message. Results showed a
significant interaction effect between valence and emoticon, F(6, 78) = 5.65, p < .001. A
negative verbal message coupled with a smile emoticon was rated more ambiguously (M =
4.20, SD = 1.42) than a negative pure message (M = 3.43, SD = 1.60) and a positi
ve pure
message (M = 3.02, SD = 1.31). A positive message coupled with a frown was signifi-
cantly more ambiguous (M = 4.08, SD = 1.39) than a positive pure message (M = 3.02,
SD = 1.31) and a negative pure message (M = 3.43, SD = 1.60).
A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA was conducted
on how positively the participants rated the message. The interaction effect between
valence and emoticon was significant, F(6, 79) = 12.93, p < .001. A negative message with
a smile emoticon was rated more positively (M = 2.40, SD = 1.28) than a negative pure
message (M = 2.04, SD = .99) and less positively than a positive pure message (M = 5.48,
SD = 1.14). A positive message with a frown was rated less positively (M = 4.33, SD =
1.37) than a positive pure message (M = 5.48, SD = 1.14) and more positively than a neg-
ative pure message (M = 2.04, SD = .99).
A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA was conducted
on how much sarcasm the writer of the message portrayed. The interaction between valence
and emoticon was significant, F(6, 79) = 14.03, p < .001. The mixed messages, positive
with a frown (M = 39.58, SD = 26.16) and ne
gative with a smile (M = 44.79, SD =
29.49), portray more sarcasm than the pure messages (respectively, M = 14.79, SD =
18.45, and M = 14.52, SD = 20.11).
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To examine the impact of emoticons on the valence of the message, it is useful to exam-
ine the neutral condition. These results show that by adding a frown to a neutral message,
this message was rated as more negative (M = 3.28, SD = 1.09) than a neutral pure mes-
sage (M = 3.85, SD = 1.09). A neutral message accompanied with a smile was more pos-
itive (M = 4.49, SD = 1.02) than a neutral pure message (M = 3.85, SD = 1.09).
Winks. A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA was con-
ducted on how positively the participants rated the message. The interaction effect between
valence and emoticon was significant, F(6, 79) = 12.93, p < .001. Adding a wink to a neg-
ative message made the message less negative (M = 2.68, SD = 1.31) than a negative pure
message (M = 2.04, SD = .99). When a positive message was accompanied with a wink,
it was equally positive (M = 5.65, SD = 1.32) to a positive verbal message without an
emoticon (M = 5.48, SD = 1.14).
A 2 (partner) × 3 (valence) × 4 (emoticon) repeated-measures ANOVA was conducted
on how much sarcasm the writer of the message portrayed. The interaction between valence
and emoticon was significant, F(6, 79) = 14.03, p < .001. The positive-with-a-wink (M =
24.27, SD = 23.55) and negative-with-a-wink (M = 47.76, SD = 29.87) mixed messages
portrayed more sarcasm than the pure messages (respectively, M = 14.79, SD = 18.45,
and
M = 15.52, SD = 20.11).
The implications of these results for the hypotheses are presented in Table 1.
Perceived Motives for Emoticon Use
A 3 (emoticon) × 7 (motive) repeated-measures ANOVA was conducted to examine the
interpretation of the motives of the writer of the message for using a certain emoticon.
Results showed a main effect of emoticon, F(2, 85) = 15.67, p < .001, and a main effect
of motive F(6, 81) = 11.21, p < .001. All emoticons differed significantly from each other.
Univariate analyses showed that most motives differed significantly from each other.
“Strengthening the message” (e.g., M
smile
= 4.93, SD = 1.74), “expressing emotion” (e.g.,
M
smile
= 4.82, SD = 1.85), “putting into perspective” (e.g., M
smile
= 4.19, SD = 1.59), and
“regulating the interaction” (e.g., M
smile
= 4.61, SD = 1.53) were the most common inter-
pretations of the motives of the user of the emoticons.
Discussion
The present study was designed to examine the role of emoticons on online message
interpretation. Walther and D’Addario (2001) concluded that emoticons have limited
impact on message interpretation. However, the present study indicates that emoticons do
influence online message interpretation. Furthermore, we examined the interpretation of
senders’ motives for adding emoticons to their messages.
The present study revealed that emoticons are useful in strengthening the intensity of a
message. A positive message with a smile is rated more positively than a positive pure mes-
sage, and a negative message with a supporting frown is more negative than a negative pure
message. The same effects are found for the amount of happiness the writer of the message
Derks et al. / Emoticons and Online Message Interpretation 385
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portrayed. This is in line with the power of nonverbal cues to intensify a verbal message in
F2F communication (Lee & Wagner, 2002).
On the ambiguity aspect of messages, the hypotheses were all supported. Mixed mes-
sages were rated significantly more ambiguously than pure messages. This is consistent
with the findings of Leathers (1986), who states that inconsistencies between verbal mean-
ings and nonverbal cues are said to be more ambiguous, which can more easily lead to mis-
communication.
What happens when emoticons contradict the valence of the verbal messages? Negative
verbal messages accompanied with a smile were interpreted more positively than a nega-
tive pure message but less positively than a positive pure message. Positive verbal messages
accompanied with a frown were rated less positively than positive pure messages and more
positively than negative pure messages. This indicates that online verbal messages have
more influence than the “nonverbal” part of the message, the emoticon. Emoticons do not
have the strength to turn around the valence of the verbal message.
The hypotheses concerning the wink emoticon are partially supported. A negative mes-
sage accompanied by a wink emoticon indeed conveys less negativity than a negative pure
message. However, the positivity of a positive verbal message with a wink was equal to a
positive verbal message alone. A possible explanation could be that because the participants
were the subjects of the messages, they were eager to believe that their performance was
good and therefore did not attach much value to a wink emoticon, for which the valence is
more debatable than that of a frown or smile.
All messages accompanied with an emoticon with a different valence than the verbal
message conveyed greater sarcasm than pure messages. The hypotheses concerning the sar-
casm issue are supported. It is possible to express sarcasm online by varying the valence of
the emoticon and the valence of the message.
In contrast with the results of Walther and D’Addario (2001), who concluded that emoti-
cons can only complement messages and do not have the strength to enhance messages, we
can conclude that emoticons do have a certain impact on message interpretation and that
they can serve some of the same functions as actual nonverbal behavior. In terms of the
known relationship between verbal and nonverbal communication, the emoticon can possi-
bly serve the function of complementing and enhancing verbal messages. We have indica-
tions that, at least with strongly valenced messages, the emoticon does not have the strength
to contradict the message.
We have also examined the interpretation of the motives for emoticon use. “Expressing
emotion,” “strengthening the message,” “regulating the interaction,” and “putting into per-
spective” were the most common interpretations of the motives. Emoticons are interpreted
as a signal of emotional information in addition to a verbal message (Thompson & Foulger,
1996), and the studies reported here show that emoticons are also used for communica-
tional ends. This is in line with evidence from F2F research that showed that facial displays
are used for emotional expression as well as to communicate social motives (e.g., Hess
et al., 1995; Jakobs et al., 1999). Emoticons can serve as nonverbal surrogates for facial
behavior and do have an impact on message interpretation.
The present study has some limitations. Participants were asked to imagine that they
received an evaluative e-mail concerning their performance. This has consequences for the
generalization of the results to actual behavior. Furthermore, participants did not actually
386 Social Science Computer Review
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interact with each other but rated e-mail messages they received without responding. For
instance, we asked them to imagine they had received the message from their best friend.
Possibly the language style of the message was different from the language style their best
friend would use. Finally, we printed the messages on paper; this might be another percep-
tion from actually e-mailing in a purpose-designed e-mail interface. In contrast with
Walther and D’Addario’s (2001) study, we used a within-subjects design containing 24 dif-
ferent situations. There is a risk for participant fatigue, though we randomized the presen-
tation of the manipulated situations. For future research, it would be useful to examine
real-life online interactions and to use a between-subjects design.
The overall conclusion of the studies presented in this article is that emoticons can serve
as useful nonverbal surrogates for facial behavior in online communication and do have an
impact on message interpretation.
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Daantje Derks is a social psychologist who is interested in the expression of emotions in computer-mediated
communication. She received her MA in social psychology at Nijmegen University and her PhD at the Open
University in 2007. She can be reached at daantjederks@gmail.com.
Arjan E. R. Bos is a social psychologist who is interested in emotions and social interaction. He received his
MA in social psychology at Free University in Amsterdam in 1995 and his PhD at Maastricht University in
2001. Currently, he is an assistant professor at the Institute of Psychology at Erasmus University in Rotterdam.
He can be reached at bos@fsw.eur.nl.
Jasper von Grumbkow is an organizational psychologist. He received his BA in psychology at Leyden
University and his MA and PhD at Groningen University. Currently, he is a professor of social sciences in
the Faculty of Psychology of the Open University of the Netherlands. He can be reached at jasper
.vongrumbkow@ou.nl.
388 Social Science Computer Review
at Erasmus Univ Rotterdam on November 18, 2008 http://ssc.sagepub.comDownloaded from
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Chapter
Chapter 3 reviews a range of specific theoretical frameworks that are suited to explore key mechanisms of emoji processing. This is distinctive from Chap. 2 in respect of identifying specific theories rather than broader approaches. This helps better explain how the features of these theoretical frameworks might be operationalised within research to address certain research questions and used within certain paradigms. Theories are situated under broader approaches of emotional processing, cognitive processing and interpersonal processing. Examples of theoretical frameworks include: The Associative-Propositional Evaluation Model (Gawronski & Bodenhausen, Psychological Bulletin 132:692–731, 2006; Social Cognition 25:687–717, 2007), Broaden-and-Build theory (Fredrickson, Review of General Psychology 2:300–319, 1998), Reinforcement sensitivity theory (RST; Gray, The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. Clarendon Press, 1982; Cognitive neurochemistry:171–190. Oxford University Press, 1987), Conceptual Metaphor Theory (Lakoff & Johnson, Metaphors we live by. University of Chicago Press, 1980), Perceptual symbol systems framework (Barsalou, Behavioral and Brain Sciences 22:577–660, 1999), Sensory-semantic model (Nelson, Levels of processing in human memory:45–76. Erlbaum, 1979), Dual Coding Model (Paivio, Imagery and verbal processes. Holt, Rinehart and Winston, 1971), Prototypical Model of Picture and Word Processing (Snodgrass, Processing of visible language. Nato conference series, Vol. 13. Springer, 1980), Social Information Processing Theory (Walther, Communication Research 19:52–90, 1992), Emotion As Social Information Model (EASI; Van Kleef, Current Directions in Psychological Science 18:184–188, 2009), Media Richness Theory (Daft & Lengel, Management Science 32:554–571, 1986), Media Naturalness Theory (Kock, IEEE Transactions on Professional Communication 48:117–130, 2005; Applied evolutionary psychology:380–398. Oxford University Press, 2011), Channel Expansion Theory (Carlson & Zmud, Academy of Management Journal 42:153–170, 1999), Elaboration Likelihood Model (Petty & Cacioppo, 1986) and the Realistic Accuracy Model (Funder, Psychological Review 102:652, 1995). Chapter 3 includes a summary of key take-away points which synthesise which theoretical framework might be best suited to understanding different types of questions about how we process emoji.
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Chapter
This chapter provides an account of emotional cues, cue combinations or channel combinations, and emotion as a process. . Cue and channel combinations depend on sex and ethnicity and whether the emotion is expressed in public or private. The information carried by cues does not break out into categories of cues and channels. Emotion as a process has some emotion-eliciting event or events that generate certain appraisals (novelty, valence, control), action tendencies, expressions, and processes by which events are then dominated. More often, the emotion falls in between anger and fear, or is a combination of envy and hate, or is vague, ill defined, and hard to label. Implication of the process perspective is that emotions are not snapshots that “click” on and off. They may come on gradually, evolve, dissipate, return, and just generally make interpretation more difficult because they change.
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This experimental study examined the effects of pictographs (typographic symbols typically used to express emotion) and quoting (responding to a message by including full or partial quotes from that message) on the perception of flaming (hostile verbal behavior) in electronic mail. It was hypothesized that (a) the presence of pictographs would reduce perceptions of flaming and (b) the use of quoting would increase perceptions of flaming. The results were generally supportive of the first hypothesis. A significant moderating effect on perceptions of flaming was observed in messages with pictographs, although that effect diminished as the intensity of hostility increased. Limited support was found for the second hypothesis; results suggest quoting may increase perceptions of flaming only when messages are overtly hostile. In addition to these findings, the study advances a clearer understanding of when a message becomes a flame, as the results suggest flaming is most clearly associated with the expression of antagonism.
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