motivations for the use of “smilies” as discourse markers in twitter messages and their effect...

Motivations for the use of “Smilies” as discourse markers in Twitter messages and their effect on the

frequency of co-occurring pronominal phrases

Introduction

Emoticon use has grown rapidly over the past 10-15 years in correlation with the explosion of online

communications and sms text messaging. What started as a niche habit, associated with other forms of ‘text-

speak’ and ‘net-lingo’ is now fully entrenched into the mainstream and can be found readily across all

aspects of written computer-mediated-communication. From offices to chat-rooms, the emoticon has become

sedimented and normalised as a discourse marker used to indicate a variety of emotional affect including

irony, humour, anger, solidarity or pleasantry. Twitter is the relative ‘new-kid-on-the-block’ and is

multimodal, acting as public broadcasting, blogging and IM service.

Aims

The aim of this study is to shed light on the kind of linguistic environment in which emoticons occur. At the

most basic level, for example, the presence of a smiley “:)” is likely to coincide with an increased likelihood

of language associated with positive emotion. The data presented here shows also; gratitude, the sweetening

of requests (“follow” / “please”) and interestingly, use of the 2nd person singular “you” and to a lesser

degree its shortened counterpart “u”.

Literature Review - Motivations

Interviews conducted by Shortis (2013), have shown repeatedly that people we might bracket as normative

in their approach to language use, who consider reworking of spelling in new media as an irritating

obfuscation, still widely consider the emoticon as an important discourse marker, and in many ways an

addition to the punctuation set. Those who value highly clarity of expression, endorse emoticon use across a

variety of media and social context.

Provine et al (2007) describe emoticons as ‘punctuating website text messages’ in a manner comparable to

the way ‘laughter “punctuates” speech’ and specifically casual speech;

“Emoticons are an unnecessary and unwelcome intrusion into well-crafted text. But emoticon use is

better contrasted with colloquial speech than formal writing or literature” (Provine et al., 2007).

Method

Two corpora were gathered from Twitter.com by continually scrolling down the page to force it to keep

displaying new tweets spanning back in time and then copy/pasting the resulting list of tweets into a plain

text document. This process was performed four times for each corpus, each time for about 10 minutes. Each

resulting corpus contains approximately 150,000 words. All_Eng corpus contains all tweets composed in

Exam number: Y0097746

English whilst the SMILE corpus contains all tweets composed in English that include the emoticons :), :-), :D

and :P. The corpuses were then put through Wordsmith Tools, to generate Wordlists and concordances. This

data was then put alongside a wordlist of the most frequently occurring words in Twitter messages compiled

by the OUP (Oxford University Press) based on 1.5 million tweets, to validate the rates that were being

shown from All_Eng corpus. On the whole, the results were comparable.

Results

The table below shows the proportion of each smily variant in a corpus of 150,000 words (Approx. 9.5k

Tweets) filtered to contain only tweets that used some emotionally positive smiley variant ( :( and :-( not

included). These rates are comparable to other findings in the new-media literature, with :) acting as the

default, go-to, smiley, :D for emphasis, :-) more elaborate and :P to denote sarcasm.

Smiley Frequency Percentage

:) 6527 68.4%

:D 1461 15.3%

:-) 741 7.8%

:)) 413 4.3%

:P 396 4.2%

Total 9538 100.0%

Table 1: Percentages of Smiley variants

Dresner and Herring (2010) state that the emoticon ‘can be viewed as an expansion of the text in the same

way that, for example question marks and exclamation marks are’. In All_Eng Corpus the smily face occurs

with about twice the frequency of the exclamation mark (291/124).

Similarly, the majority of smilies occur at the end of a tweet with 4087 of the 6527 tweets with :) having

nothing at all following it with approximately another 1000 followed by further finalisation of utterence (‘x‘,

‘xx‘, #name etc). So roughly 75% occurring at the end of a phrase, interestingly not as many as Provine et al

(2007) who found 99% emoticons occur at end of phrase across more general website texts. The smiley

represents roughly 0.2% of all tokens used on Twitter. What is interesting is that this ratio it fairly constant

whether a corpus is filtered to contain only tweets with non-standard spellings or is open to all English

tweets.


(1) Seems like you just don't like me anymore :')

(2) actually a lot of people hate me lol :-) :-) :-)

(3) and idk i want to become better friends with you :)

(4) @name follow back. Thanks :)

(5) At least work was fun today :)

(6) Going to the park with the children, seems like a nice day :)

(7) very good thank you ;-) how's yourself name?X

Table 2: examples of Smiley usage

The above examples demonstrate the kind of functions served by the smiley. They are representative of the

proportion of phrase-final versus phrase medial. In line with Dresner & Herring’s (2010) drawing of a

parallel between emoticons speech act theory, smilies mirror real-world smiles to some degree. They serve to

show that an utterance is intended jokingly as in (1) and (2), to indicate that happiness (3) and (6), to sweeten

a request (4), to disambiguate (5) - in this instance to show that the statement is not sarcastic and the ‘winky’

‘;)’ used here as a greeting, and to delineate between discourses.

Word OUP SMILE ALL_ENG RATIO: SMILE/

ALL_ENG

thanks 0.09% 0.31 0.07 0.23

please 0.03% 0.28 0.08 0.28

follow 0.03% 0.43 0.14 0.32

birthday 0.02% 0.15 0.06 0.38

good 0.20% 0.43 0.17 0.40

awesome 0.04% 0.05 0.02 0.46

happy 0.05% 0.22 0.10 0.47

love 0.12% 0.47 0.25 0.53

hope 0.05% 0.11 0.06 0.55

cool 0.04% 0.05 0.03 0.59

see 0.10% 0.17 0.10 0.61

would 0.09% 0.12 0.08 0.66

you 0.75% 1.66 1.10 0.66

u 0.07% 0.27 0.19 0.71

going 0.13% 0.10 0.09 0.82

your 0.20% 0.37 0.31 0.83

are 0.25% 0.28 0.24 0.84

me 0.35% 0.76 0.65 0.86

so 0.26% 0.43 0.37 0.86

have 0.33% 0.34 0.30 0.89

lol 0.11% 0.19 0.18 0.92

Table 3: Lexical items showing increased frequency in SMILE corpus from most increased to least with pronominal phrases highlighted


This table shows a marked increase in the frequency of 2nd person singular/plural as well as possessive.

Interestingly the 1st person singular ‘me’ shows and increase, whilst as shown in the table below, the 1st

person singular “I” shows little or no change. Perhaps this might point to smilies being used to make the

dialogue more ‘chat-like’. Tagliamonte & Dennis (2008, p.13) report that “first- and second-person pronouns

are more frequent in speech than in writing” and that earlier research from Yates (1996) on CMC at computer

conferences reflected this. With ‘you’ referencing the person with whom a speaker is interacting, it is perhaps

not a surprise that an increase in simulated outward gestures might be seen.

Word OUP SMILE ALL_ENG RATIO: SMILE/ALL_ENG

do 0.33% 0.22 0.22 0.99

it 0.76% 0.57 0.56 0.99

but 0.24% 0.23 0.23 1.00

and 0.85% 0.74 0.74 1.00

she 0.06% 0.08 0.08 1.03

for 0.71% 0.57 0.58 1.03

i 1.79% 1.71 1.77 1.04

be 0.29% 0.37 0.38 1.04

my 0.59% 0.67 0.70 1.04

just 0.32% 0.28 0.30 1.06

Table 4: Lexical items showing no change in SMILE corpus with pronominal phrases highlighted.

Word OUP SMILE ALL_ENG RATIO: SMILE/ALL_ENG

a 1.33% 1.08 1.19 1.11

with 0.36% 0.26 0.30 1.14

to 1.56% 1.12 1.33 1.19

on 0.60% 0.36 0.43 1.22

this 0.30% 0.30 0.37 1.23

like 0.20% 0.23 0.29 1.25

that 0.49% 0.31 0.39 1.25

omg 0.02% 0.04 0.05 1.27

funny 0.02% 0.03 0.04 1.32

out 0.23% 0.13 0.18 1.37

the 1.82% 1.10 1.51 1.37

not 0.25% 0.19 0.27 1.38

he 0.11% 0.07 0.10 1.38

is 0.83% 0.46 0.66 1.43

in 0.76% 0.45 0.67 1.51

of 0.75% 0.39 0.60 1.55

up 0.22% 0.16 0.26 1.61

at 0.37% 0.18 0.28 1.62

bad 0.04% 0.03 0.05 1.89

why 0.08% 0.06 0.11 1.89

they 0.13% 0.06 0.12 1.92

Table 5: Lexical items showing decreased frequency in SMILE corpus from most increased to least with pronominal phrases highlighted


The above two tables show a small but clear preference for not using smilies in combination with masculine

or neutral pronouns ‘he’ and ‘they’, while ‘she’ is unchanged. Perhaps people actively avoiding combining

emoticons with male pronouns or maybe 3rd person pronouns are generally reduced by the use of

emoticons, with the female form being boosted back up to normal levels by association of their feminine

characteristic (Tossell et al., 2012).

Conclusion

Much is beyond the scope of a 1000 word paper and this is really a snapshot of a much larger view.

Questions that beg answering are; to what extent are online texts marked by their absence of discourse

markers? In which linguistic situations does a pressure to include some form of emoticon arise, how

widespread is this pressure and in such situations, how far can a decision to avoid their use be interpreted as

a deliberate veering away from a perceived childish or immature behavior as Tagliamonte (2008) describes.

Word Count = 1070


References / Bibliography

Bays, H. (1998). Framing and face in Internet exchanges: A socio-cognitive approach. Linguistik online, 1(1).

Derks, D., Bos, a. E. R., & Von Grumbkow, J. (2007). Emoticons and Online Message Interpretation. Social

Science Computer Review, 26(3), 379–388.

Dresner, E., & Herring, S. C. (2010). Functions of the Nonverbal in CMC: Emoticons and Illocutionary Force.

Communication Theory, 20(3), 249–268.

Tossell, C. C., Kortum, P., Shepard, C., Barg-Walkow, L. H., Rahmati, A., & Zhong, L. (2012). A longitudinal

study of emoticon use in text messaging from smartphones. Computers in Human Behavior, 28(2), 659–663.

Provine, R. R., Spencer, R. J., & Mandell, D. L. (2007). Emotional Expression Online: Emoticons Punctuate

Website Text Messages. Journal of Language and Social Psychology, 26(3), 299–307.

Shortis, Tim (2013). Personal email correspondence.

Tagliamonte, S., & Denis, D. (2008). Linguistic Ruin? LOL! Instant Messaging and Teen Language. American

speech, 83(1), 3–34.


Appendix

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Figure 2: Line chart displaying token frequency as percentage of total word count in SMILE and ALL_Eng


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