Analysis Text Messages Essay Examples

Text messaging, short message service (SMS) or ‘texting’ continues to be a popular means of communication, among young people in particular. A report by Lenhart, Ling, Campbell, and Purcell (2010) highlighted the rapid increase in text messaging in the United States, where 72% of teenagers use text messaging, compared to 51% in 2006. In a British survey, 52% of young people aged 11-18 (n = 1000), along with 28% of adults aged 18-65 (n = 2000), named texting as the most important form of communication that they use to stay in touch with friends (Mobile Life Report, 2008); for the young people surveyed, texting ranked above instant messaging (17%), e-mail (12%), calls via mobile (9%) or landline (10%), and letters (0%). A British survey of 2117 adults shows the increasing popularity of texting from 2005 to 2010, with 62% of those aged 16-24 preferring texting over other means of communicating with friends (Ofcom, 2011). Ling's (2010) cross-sectional analysis suggests that texting follows a life-phase pattern, with older teens and those in their early 20s making the most use of the medium, with usage dropping off with age.

Texting is a fast, cost-effective, personal and nonintrusive means of communicating (see Ling, 2005). It is near-synchronous, and associated with distinctive styles of conversation and writing features such as ‘textisms’ (Carrington, 2004; Rettie, 2009). Textisms are language variants such as abbreviations and nonstandard forms of words (Crystal, 2008a,b; De Jonge & Kemp, 2010; Plester & Wood, 2009), and include features such as letter and number homophones (c for ‘see’, 2 for ‘to’), contractions (txt for ‘text’) and nonconventional spellings (nite for night; see Kemp & Bushnell 2011; Plester et al., 2009; Thurlow & Brown 2003).

The limited analyses of text language that are available suggest that most of the language is standard and that distinctive or nonstandard forms occur alongside standard ones (Crystal, 2008 a,b). As Shortis (2007) points out, text messaging has “de-regulated what counts as English spelling rather than altered spelling itself” (p.21). Carrington (2004) borrows the term ‘squeeze-text’ to describe the principal features of text language. Words may be shortened to the minimum syllable length, often by removing vowels. Articles and conjunctions may be omitted, and numbers or letters may be substituted for graphemic units e.g., gr8 for ‘great,’ 4 for ‘for,’ 2 for ‘to,’ c for ‘see,’ or sum1 for ‘someone.’ Common phrases may be represented by acronyms (e.g. LOL, ‘laugh out loud’). Capital letters might be omitted or used for emphasis. End-message punctuation may be absent. Various other abbreviations and nonstandard forms have been noted (see Carrington, 2004; Crystal 2008a; Drouin & Davis, 2009).

Letter/number homophones (e.g. l8r for ‘later’, or w8 for ‘wait’), contractions, and emoticons are less frequently recorded in analyses of naturalistic text messages than media representations of text language would suggest (Ling & Baron, 2007; Thurlow, 2006). The variety and complexity of emoticons has, in particular, been exaggerated, with the ‘smiley’ and ‘frown face’ ( :-) or and :-( or :- :- ) being the main emoticons used and accounting only for a modest proportion of message content (e.g. see Ling & Baron, 2007; Thurlow & Brown, 2003). Similarly, the main typographic symbol used in texts is an ‘x’ to signal affection, a convention commonly found in informal writing (e.g. see Thurlow & Brown, 2003). Thurlow and Brown's (2003) data show a low frequency of emoticons (:-) ), typographic symbols (xxx), and letter/number homophones (gr8/great) in comparison to nonconventional spellings (nite/night), accent stylizations (ello/hello), and onomatopoeic spellings (yay!, haha), forms that suggest the influence of speech on the medium.

Estimates of the prevalence of textisms within text messages vary. Accounts agree that the majority of text language is standard form, and the nonstandard forms used are generally ‘semantically recoverable’ (Thurlow & Brown, 2003); after all, the texter will want to ensure that they are understood (Crystal, 2008a). The data contrast with media portrayals of text messages as an indecipherable code (see Thurlow, 2006). Ling and Baron's (2007) sample of American university students' text messages (191 texts) contained less than 5% textisms. Thurlow and Brown's (2003) analysis of 544 texts collected in Wales produced a higher estimate, with textisms accounting for 19% of total message content (see also Thurlow & Poff, in press). Crystal (2008) suggests that about 10% of total message content is accounted for by textisms. There may be further variation across languages (Bieswanger, 2007; Döring, 2002 cited in Bieswanger, 2007; Ling, 2005).

The prevalence of textisms within text messages has been exaggerated in the media, with some descriptions treating text messaging as if it were a ‘foreign’ language (e.g., see Crystal, 2008 a,b; Jones & Schieffelin, 2009; Thurlow, 2006). Consequently there is much concern over the impact of the use of such forms on young people's literacy, a concern that is without strong empirical support (e.g., Plester, Wood, & Bell, 2008; Plester, Wood, & Joshi, 2009). Textism use arguably demonstrates an appreciation of the sounds of language (Crystal, 2008, a,b; Jones & Schieffelin, 2009; Plester & Wood, 2009; Tagliamonte & Denis, 2008; Thurlow, 2006). Plester and Wood's (2009) study of preteens found no negative effects on literacy for young users. Some studies have reported a positive effect of texting on children's literacy skills (e.g. Plester et al., 2009), although phonological skills may mediate some of that relationship (see Wood et al., 2011). Some studies have noted negative effects on literacy skills, however. Rosen, Chang, Erwin, Carrier and Cheever's (2010) study of young adults showed a negative association between self-reported textism use and formal writing, while there was a positive association with informal writing. However, self-reported textism use was quite low in this case, and may or may not reflect actual use of textisms.

Concerns over effects on literacy have recently been supported by experimental research demonstrating processing costs associated with textisms. Experimental studies examining the reading of text messages have tended to focus on the nonstandard forms, even though much of the language in text messages is standard. For example, Berger and Coch (2010) compared event-related potentials (ERPs) in response to semantic anomalies in texted and standard English in young adults and found that responses to sentences which contained textisms mirrored those seen in nonnative language processing. However, the text stimuli were ‘translations’ of standard English sentences used in ERP studies and contained sequences not generally found in text messages (e.g. ‘c@’ for ‘cat’). The context in which text messaging language is encountered was not considered when selecting the text stimuli. Berger and Coch (2010) note that the sentences used, “while relatively typical in standard English, are more unlikely to be on topics typically discussed in everyday texting” (p.145).

Similarly, Perea, Acha and Carreiras (2009) found a reading cost for sentences containing textisms when comparing young adults' eye movements when reading texted and standard Spanish. They selected words based on frequency in an SMS dictionary, but the sentences used were unlikely to be encountered in real text messages (for example, sentences included “finish the soup at once” and “we'll go to the concert on my bike”).

Kemp (2010), using a textism translation/generation task, found that messages using textisms were faster to write than those in standard English, but they took nearly twice as long to read, and were associated with more reading errors. In this case, participants read preprepared sentences or wrote to dictation; again, the texted sentences exaggerated features found in real text messages. For example, a standard English message had a texted counterpart with 70% textisms in a 23 ‘word’ message (“i h8 2 ask, but dnt 4get 2 txt me an aQr8 time to pu my frendz. thnx. def c u 4 dnr!”: “I hate to ask, but don't forget to text me an accurate time to pick up my friends. Thanks. Definitely see you for dinner!”). Participants had difficulty reading some of the texts. BN (for ‘being’), aQr8 (‘accurate’) and ez for ‘easy’ proved particularly problematic; in the latter example, as ‘z’ is pronounced ‘zed’ not ‘zee’ by this Australian sample, ‘ez’ would not seem a valid shortening, as Kemp notes. Kemp acknowledges that texts were longer and contained more textisms than would occur in real messages, and that texts between friends would likely produce less confusion. The low rate of intrusions of textisms into the conventional condition was also noted.

Other studies reporting reading costs associated with textisms have also used tasks that may overrepresent textisms. A study by Kemp and Bushnell (2011) used both a reading and a writing task. In the writing task, children (average age 11.5 years), were asked to type two messages that were dictated to them. In the conventional condition, the children were instructed to make sure that “all words [were] spelled correctly and with proper punctuation.” In the ‘textese’ condition, they were instructed to type as they “would normally text a friend” (p.21). Kemp and Bushnell found that the proportion of textisms used by children instructed to write messages conventionally or using textese was 3% and 35%, respectively, supporting the notion that textism use is contextually controlled. But in a parallel reading task, messages were entirely conventional or almost entirely written with textisms. For example, the conventional sentence “When will we see you tonight? Because someone left a message about your friend being sick. Are you sick too?” became “Wen wil we c u 2night? Cause some1 left a msg bout ur frend bein sik. R u sik 2?” in the textese version. The finding that both speed and accuracy were compromised in the textese condition must be interpreted in light of these differences in stimuli. Furthermore, the sentences devised were quite long; the conventional versions consisted of, on average, 20 words, or 107 characters, presented in 3 sentences.

Such studies make an important contribution to our understanding of the processing of spelling variants, but they may not always reflect real-world aspects of text messaging, such as the context of the communication, the sentence types and subject matter, and the proportion of textisms relative to standard forms. It is important to consider the extent to which the sentences used in such experiments reflect the linguistic characteristics of actual text messages.

Estimates of textisms vary in experimental studies, along with samples and methods. Kemp (2010) found that 50% of the content in generated text-messages was written as textisms, with some words always written by participants as textisms (e.g., words such as ‘are,’ ‘message,’ ‘tonight’ never occurred as standard spelling). In a parallel reading task, Kemp noted that the stimuli contained more textisms than would be found in naturalistic text messages in English, with 70% of the total content consisting of textisms. In Kemp and Bushnell's (2011) writing task, 35% of total content consisted of textisms. De Jonge and Kemp (2010) had their participants translate printed sentences with the instruction to type “as they would if sending the message to a friend.” This produced a low proportion of textisms at 14% for young adults and 15% for teenagers. Studies with children have produced estimates of 34% for text generation tasks, in which messages were composed by the children themselves based on hypothetical scenarios (Plester et al., 2009), and 58% for dictated messages, where children translated preprepared standard English sentences into textese (Plester et al., 2008). Studies in which text sentences are generated by the researchers for use as stimuli produce an even higher proportion of textisms, as noted above. By contrast, analyses of naturalistic text messages have produced lower estimates, Thurlow and Brown's (2003) estimate of 19% in British undergraduates being among the higher of these. Mobile phone handsets and predictive texting capabilities have changed considerably since Thurlow and Brown's data were collected and there is a need for a more up-to-date analysis of the language that is used in text messaging.

As Kemp (2010) noted, experimental studies are limited by the lack of data on text messaging language. In the present study, we aimed to provide such data by examining the textual characteristics of text messages supplied by a young adult English-speaking sample. Data were collected in Ireland, where text messaging is a frequently used form of communication, particularly by young people. In the first quarter of 2010, mobile phones users in Ireland sent over 3 billion text messages, averaging 192 messages per subscription per month (ComReg, 2010). The focus of the current analysis was on textisms, defined as abbreviations, lexical shortenings and nonstandard spellings (see Kemp, 2010). The level of analysis focuses on the individual text message, rather than on the sender. We examined: (i) message length in terms of sentences, words and characters per text message; (ii) prevalence and types of nonstandard spelling; (iii) textisms as a function of message length; (iv) sender and message characteristics affecting spelling choice; and (v) word frequency. The aim of the analysis was to establish, in a reasonably large naturalistic dataset, the proportion of textisms used, context effects on textism use and the amount of language that was, to use Thurlow and Brown's (2003) term, ‘semantically unrecoverable’ to the objective reader. Furthermore, the variety of textisms used was examined by means of a frequency analysis of the text messages.

Method

Data generation

Text messages were collected from a convenience sample of 139 undergraduate students (99 women and 40 men) attending university in Ireland. All participants were Irish and English was their first language. We recruited college students as participants in order to mirror the age range and educational profile of the young adults typically participating in the kinds of experimental studies outlined above (e.g. Berger & Coch, 2010; Kemp, 2010; Perea et al., 2009). The average age of participants was 22 years (SD = 4.1); the average age of the people the participants sent texts to, as reported by the participants, was 25 years (SD = 9.4). Each participant was asked to provide up to ten text messages sent in the previous week. Participants were informed that the research study was concerned with “the language used in text messages.” Participants transcribed their texts verbatim onto paper; they were instructed (verbally and in writing) to carefully reproduce the original message, paying attention to spacing between words, punctuation marks and capital letters. They also provided other details, such as their age and gender, the age and gender of the message recipient, and their relationship to the participant (e.g., friend, family, workmate etc.). Each participant also selected the purpose of the text from a number of categories (e.g. seek information, reply, make arrangement, etc.). In contrast to previous work in which the researchers have coded the texts' function (e.g. Thurlow & Brown, 2003), we had participants supply this label, so that it fitted with their intention. Participants were asked to choose messages that were genuinely representative of those they generally sent. To encourage selection of representative messages, participants were given a code to use to obscure any private information during transcription (such as a name). A total of 133 text messages used this code, which suggests that this method was successful in allowing participants to include messages that they might otherwise have chosen not to share. Any remaining identifying information (a name and phone number in just one message) was removed. Participants chose which messages they wished to divulge and they were assured of the confidentiality and anonymity of their responses.

The messages were subsequently transcribed into an electronic document. A number of ‘automated’ text messages (such as forwarded SMS advertisements and ‘chain’ texts) were removed from the set. Three messages written in languages other than English were also removed. This resulted in a set of 936 text messages, with a total of 13391 words (tokens) and 676 nonword units (e.g., symbols, emoticons, and multiple punctuation marks); 72% of the messages were sent by women, and 66% of those receiving the texts were women. The majority of content consisted of text communication between young adults, mainly among friends. The following analysis treats the individual message as the unit of analysis.

Coding

Coding of texts was based on the typology used by Thurlow and Brown (2003) to analyze naturalistic data, following that of Shortis (2001). The categories are similar to those of De Jonge and Kemp (2010), which was adapted from Plester et al. (2009). The categories of nonstandard spelling were: Accent stylization; Contractions; Emoticons and typographic symbols; G clippings; Initialisms; Letter/number homophones; Misspellings; Missed punctuation (excluding end-message punctuation); Missed capitalization; Other clippings; Onomatopoeic/ exclamatory expressions; Nonconventional/phonetic spellings; Semantically unrecoverable words; Shortenings. Examples of each category, along with definitions, are presented in Table 1. After initial coding of the 936 messages, a sample of messages (approximately 10% of the set) was independently coded by a second rater. Interrater agreement was 98% across the categories noted in Table 1.

Missed capitalizationA words is spelled without appropriate capital letterjohn, i'd72822.09
Accent StylizationA word is spelled as it is pronounced in casual speechwantz, wanna, gona, cuz, dis, ds61518.66
Letter/number homophonesA letter or number used to take the place of a phoneme, syllable, or word of the same sound2 (to), 4 (for), l8r, u, r (are), c (see), gr8, ru, 2ni (tonight), 2gether42913.02
Missed punctuationOmitted periods, and spelling with missing apostrophedont, cant, wont, ill36010.92
ContractionsOmitting letters from the middle of wordsTxt, wknd, dnt, plz, bday, gng1685.10
Phonetic/ nonconventional spellingsA spelling of a word from soundfone, nite, luk, buks, cum1835.55
G ClippingsOmitting the final g in a word ending ‘ing’goin, talkin, comin1715.19
Other clippingsOmitting other final letterstel, I'v, hav, wil, com1564.73
Onomatopoeic/ exclamatoryA nonword sound-based exclamationHa, arrrgh, woohoo, yay1564.73
ShorteningsOmitting the end of a word, losing more than one letterProb, bro, mon, tues1384.19
MisspellingsMisspelled wordsdont't (don't), juut (just), remeber (remember), thought (taught)1263.82
InitialismsA word or group of words represented by initial letterstb = text back, gf = girlfriend, poa = plan of action, nntr = no need to reply391.18
Semantically unrecoverableWords apparently not correct in current context, or where texter's intended word is not clear 270.82
Total  3296100

Results

The following analysis considers message length, the prevalence and types of nonstandard spelling, the effect of message length on textism use, the effect of sender and message characteristics (including the purpose/ function of the text message) and word frequency.

  1. Message length Word count was calculated manually for each individual text message, as automated word counts may not count lexical items that are joined by a punctuation mark (e.g. ‘ok.see you’ would be counted as two words, not three). The word count included words and lexical substitutions for words; for example, the text ‘i wish i ws der’ (‘I wish I was there’) contained five ‘words’ or lexical items. Additional characters, such as emoticons and punctuation marks, were not included in the word count. Character count was automated and included spaces. The average length of messages was 14.3 words (SD = 12.0); this is consistent with Thurlow and Brown's (2003) 14 words per message estimated using an automated word count. On average, messages used 70 characters, with considerable variation in character count (SD = 59.4). Seven percent of messages exceeded the notional 160 character per message limit. Messages consisted of, on average, 2 sentences (SD = 1.46), with a median of one sentence.
  2. Prevalence and types of nonstandard spelling Of the 936 texts, 158 had completely accurate spelling, that is 17% of the messages contained no textisms or nonstandard forms of any kind (including missed capitals or punctuation, etc). Of the 13391 words in the dataset, 10222 (76%) were judged to have entirely standard spelling; this count used stringent criteria, including only those words that were spelled, punctuated and capitalized correctly. The requirement for capitalization was, in particular, rather a strict criterion; capitalization is often abandoned in text messages as it can require several keystrokes on some handsets.

Categories of nonstandard spelling, with definitions, examples and number of occurrences are shown in Table 1. A small number of acronyms (17) appeared across the set; these were standard acronyms (e.g. CD, DVD, BBQ) rather than initialisms and are therefore not considered to be nonstandard spellings here. Nonstandard spellings were defined as spelling deviations of the following types (see Table 1 for definitions): letter/number homophones; onomatopoeic words; contractions; shortenings; g-clippings; other clippings; initialisms; nonconventional/ phonetic spellings; accent stylizations; misspellings; missed punctuation; missed capitalization; unrecoverable forms. Onomatopoeic words are not necessarily nonstandard, and are often accepted in other forms of writing; however, to maintain a strict set of criteria so as not to underestimate the number of nonstandard forms, we included them here.

There were 3296 instances of nonstandard spelling, accounting for 25% of the total word content. This is a marginal overestimation, as a number of words (less than 1% of the set) were coded more than once (e.g. an item like ‘2moz’ uses both a homophone and an accent stylization, while ‘id’ includes a missed capital as well as a missed apostrophe). Missed capital letters accounted for the highest proportion of nonstandard spellings, at 22% of the total. Consistent with Thurlow and Brown's (2003) analysis of British text language, accent stylizations were also a frequent deviation from standard spelling, accounting for 19% of all nonstandard spellings. Letter/ number homophones were also frequently employed, with 429 occurrences, accounting for 13% of the total nonstandard spellings. There was considerable variety within this category; for example, the word ‘tomorrow’ appeared as 2morrow, 2morro, 2moro, 2mro, 2mo, 2m, 2moz. The categories of nonstandard spelling are detailed in Table 1.

There were 676 instances of emoticons and typographic symbols, including multiple punctuation marks, which were the most prevalent type within this category. The use of a single x (n = 162) or sequence of x's (n = 100) was also prevalent. Of the 936 text messages, 107 (11%) used emoticons, with 136 emoticons in total. A small variety of emoticons was encountered, with smiley and frown faces accounting for the majority of instances. Of the messages containing emoticons, 81% were sent by women.

A large number of nonstandard spellings were due to incorrect (midmessage) punctuation (360, or 11% of the total nonstandard spellings), the majority of which consisted of omitted apostrophes (349). Of the messages in which an apostrophe might have been used (611 messages), 43% were correct. The others omitted an apostrophe, most commonly within the possessive (e.g. writing ‘Johns place’ instead of ‘John's place’). Other common errors included words such as I'm and it's.

Ten per cent of the messages contained more textisms than standard spellings. Just 27 ‘words’ (or 0.2% of the total content) were semantically unrecoverable to the researchers; in at least some cases, these items may well have been understood by the texters themselves, or might have been clear had the context been reinstated.

  • 3.Does message length affect textism use? There was a strong positive correlation between the number of lexical items (words and word substitutes) in the text message and the number of standard spellings present (r = .95, p < .01), as well as with each of the nonstandard spelling types (see Table 2), suggesting that longer messages (i.e. more words) provide more opportunity for both standard and nonstandard spellings. There was no correlation overall between message length and the proportion of the text that used standard spelling. However, of the messages that contained more textisms than standard spellings (10% of the set), a majority (67%) were short messages, below the average length of 14.3 words.
  • 4.Sender and message characteristics affecting spelling Overall, there were more messages in our dataset that had been sent by women (677 messages from 99 senders, accounting for 72% of the data) than by men (259 messages from 40 senders), which must be taken into account when considering gender differences. There was no difference in message length, with messages from men using an average of 13.9 (SD = 11.6) words per message, while those from women used 14.5 words (SD = 12.1). Messages sent by women contained a higher proportion of nonstandard spellings; the effect size here was small, however. On average, men's messages consisted of 78% standard spelling (SD = 20.3); for women, 74% of content (SD = 21.4) was standard, t(934) = 2.4, p = .017, r = .01.
1. Words          
2. Standard spellings.95b         
3. Homophone.30b.15b        
4. Onomatopoeic.29b.24b–.04       
5. Shortening.25b.19b.07a.03      
6. Contraction.23b.07a.28b.02.06     
7. G Clippings.39b.28b.18b.17b.15b.17b    
8. Other Clippings.33b.20b.15b.07a.14b.25b.31b   
9. Initialisms.20b.17b.07a.01.03.04.10b.10b  
10. Nonconventional/ phonetic spellings.27b.10b.42b–.00.13b.33b.21b.21b.08a 
11. Accent stylizations.38b.18b.31b.11b.14b.45b.32b.35b.04.40b

Some gender differences were suggested within the categories of nonstandard spelling, with small effect sizes in each case. Women were more likely than men to use (or to contribute texts containing) emoticons and typographic characters, such as multiple punctuation, t(934) = 3.14, p = .002, r = .1, shortenings, t(934) = 2.1, p = .033, r = .03, and initialisms, t(934) = 2.9, p = .003, r = .1 (see Table 3). While there was no overall gender difference in the use of accent stylizations, this form was more likely to occur in same-sex friend dyads i.e. men texting men and women texting women, F(1,930) = 5.64, p = .018, r = .2. No interactions between the gender of sender and receiver were observed for the other categories of spelling. However, the underrepresentation of data from men must be considered here; it may be that men text less overall or the texts collected here may not be entirely representative.

Number of words13.9 (11.6)14.4 (12.1)nsp > .5.09
Emoticons and typographical symbolsa0.49 (1.1)0.81 (1.5)sp < .01.12
Standard spellings11.05 (9.5)10.9 (9.7)nsp > .8.01
Homophones0.38 (0.9)0.49 (1.0)nsp > .1.05
Onomatopoeic0.17 (0.6)0.16 (0.5)nsp > .3.03
Shorteninga0.09 (0.32)0.17 (0.5)sp < .01.07
Contraction0.12 (0.5)0.2 (0.6)nsp < .05.07
G clippings0.2 (0.5)0.18 (0.5)nsp > .6.02
Other clippings0.12 (0.4)0.18 (0.6)nsp > .1.05
Initialismsa0.01 (0.1)0.05 (0.2)sp < .01.09
Non-conventional spellings0.17 (0.4)0.2 (0.6)nsp > .3.03
Accent stylizations0.54 (1.1)0.71 (1.2)nsp > .05.06

Participants also noted the purpose or function of the text from nine categories (e.g. to seek information, reply, make an arrangement, express humour etc; see Figures 1 and 2). The purpose was not given for 3 of the 936 texts. The most frequent reason given for sending the text was to seek information or make a request, accounting for 27% of all the text messages. Making arrangements (16.5%) and replying to messages (17%) were also frequent, as was sharing information (13%). This sample of text messages therefore would seem to underrepresent those sent for the purposes of maintaining or supporting relationships; for example, the greetings category accounting only for 6% of the total. This suggests a certain level of censorship when participants chose which messages to contribute. It may be that texts which support relationships were more personal and participants may have been reluctant to share them.

A one-way analysis of variance was conducted to examine differences in message content across the nine categories of text (see Figures 1 and 2). This identified differences across a number of the variables, including message length, F(8,934) = 24.24, p < .001, number of emoticons and typographic symbols, F(8,934) = 17.9, p < .001, standard spellings, F(8,932) = 21.45, p < .001, onomatopoeic expressions, F(8,934) = 16.54, p < .001, shortenings, F(8,934) = 2.31, p = .031, g clippings, F(8,934) = 9.69, p < .001, other clippings, F(8,932) = 4.29, p < .001, initialisms, F(8,933) = 3.7, p < .001, and accent stylizations, F(8,934) = 8.019, p < .001. Most of these differences simply reflected variation in message length; post hoc tests showed that messages sent to ‘share information’ or that participants identified as having ‘multiple’ functions were longer and contained more punctuation, emoticons, clippings, initialisms, and accent stylizations than other message categories (ps < .01).

There was no significant difference in the proportion of the text that used standard/nonstandard spelling across the categories, F(8,933) = 0.75, p = .647 (see Figure 1), despite differences in message length; the proportion of standard spelling varied from 71% in messages sent to thank someone to 79% in messages sent to share information. Similarly, shortenings, contractions, nonconventional spellings, and letter/number homophones did not differ significantly across the text message categories. These data might be taken to suggest that the texter's choice of a particular textism depends on the context of the message (see Figure 2); however, it may be that some texters used, or contributed to the study, particular categories more than others, which would affect the distribution of textisms across the categories.

  • 5.Word frequency A computer program (TextSTAT; Hüning, 2002) was used to perform a simple frequency calculation (i.e. without tagging part of speech). The program identified 3073 distinct lexical items, 1896 of which received only one mention. The 100 most frequent words used accounted for 46% of the total content. Of the 100 most frequently used words, 16 were nonstandard spellings: u, i, hey, ya, yeah, im, 2 (for ‘to’), d or D (for ‘the’), 4 (for ‘for’), b (for ‘be’), Im, i'm, ur (for ‘your’), goin, U. The numbers 2 and 4 appeared alone and within a number of words as homophones.
Article Information

Authors:
Chaka Chaka1
Mampa L. Mphahlele1
Charles C. Mann1

Affiliations:
1Department of Applied Languages, Tshwane University of Technology, South Africa

Correspondence to:
Chaka Chaka

Email:
chakachaka8@gmail.com

Postal address:
Private Bag x680, Pretoria West 0001, South Africa

Dates:
Received: 16 Mar. 2015
Accepted: 17 Aug. 2015
Published: 04 Nov. 2015

How to cite this article:
Chaka, C., Mphahlele, M.L. & Mann, C.C., 2015, ‘The structure and features of the SMS language used in the written work of Communication English I students at a university in South Africa’, Reading & Writing 6(1), Art. #83, 9 pages. http://dx.doi.org/10.4102/rw.v6i1.83

Copyright Notice:
© 2015. The Authors. Licensee: AOSIS OpenJournals.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The structure and features of the SMS language used in the written work of Communication English I students at a university in South Africa
In This Original Research...

Open Access

Abstract
Introduction
Relevant studies
Research methodology
Research questions
Participants and sampling technique
Materials and data collection process
Data analytic procedure – Morphosyntactic analysis
Findings
Morphological processes at play in participants’ text messages
   • TMA
   • TMB
Syntactic processes at play in participants’ text messages
Type of SMS language features in participants’ writing samples
Discussion
Participants’ text messages, and their morphological structures
Participants’ text messages and their syntactic structures
Participants’ writing samples and types of SMS language features
Conclusions and recommendations for further studies
Acknowledgements
   • Competing interests
   • Authors’ contributions
References

Employing an explanatory design, this study set out to investigate the morphosyntactic structures of the SMS language of Communication English I students, and the types of SMS language features used in their written work at a university of technology in South Africa. The study randomly sampled 90 undergraduate students (M = 40; F = 50) enrolled for a national diploma programme during the first academic semester in 2013. Their ages ranged from 19–22 years; they all spoke English as a second language, whilst having one of the five black South African languages as their home language. The study had two types of data: participants’ mobile phone text messages (in two sets), and their writing samples. Two of the findings of the study are: the morphological structure of the textisms used in the participants’ text messages deviated from that applicable to formal, standard English, whereas much of their syntactic structure did not; and, the frequency and proportion of textisms in participants’ writing samples were lower than that reported in studies by Freudenberg (2009) and Rosen et al. (2010).

Short message service (SMS) language – also known as text messaging – has become a subject of a number of studies in recent times. One of the focal areas of these studies has been on features of SMS language, especially of young or teenage users, and how such features may affect these users’ writing or literacy skills, or their spelling proficiency. Amongst the studies that have investigated how SMS language affects young users’ writing or literacy skills are: Aziz et al. (2013); Dansieh (2011); Deumert and Masinyana (2008); Drouin and Davis (2009); Durkin, Conti-Ramsden and Walker (2011); Freudenberg (2009); Geertsema, Hyman and Van Deventer (2011); Njemanze (2012); Plester and Wood (2009); Rosen et al. (2010); Shafie, Azida and Osman (2010); and Vosloo (2009). In a similar vein, some of the studies that have examined the relationship between young users’ SMS language and spelling are: Bushnell, Kemp and Martin (2011); Farina and Lyddy (2011); Lyddy et al. (2013); Powell and Dixon (2011); and Varnhagen et al. (2010). In addition, there are emerging studies that are investigating the relationship between young users’ use of SMS language and some aspects of grammar: Adebileje (2014); Kahari, Mutonga and Ndlovu (2013); Nweze (2013); Oladoye (2011); Ong’onda, Matu and Oloo (2011); Wood, Kemp and Waldron (2014); and Wood et al. (2014).

The current study set out to examine the morphological and syntactic (morphosyntactic) structures and features of SMS language evident in the written work of undergraduate students enrolled in a national diploma course, Communication English I, at a university of technology in Gauteng, South Africa. Its overriding contention was that, overall, students - including the ones whose texting was investigated in this study - do not use as much texting in their formal writing, or do not transfer as much texting to their formal writing, as is often reported (see, for example, Dansieh 2011; Geertsema et al.2011; Yousaf & Ahmed 2013). In this instance, its four major goals were to:

  • Establish whether the morphosyntactic structures used by students in their text messages conformed to, or deviated from, the Standard English syntactic elements.
  • Identify the morphosyntactic structures of the SMS language used by these students in their written work.
  • Identify the types of SMS language features these students used in their written work.
  • Determine the frequency of textisms present in these students’ writing samples.

These goals also constituted the purpose of the study. This purpose was informed by the fact that many studies on SMS language, in general, and SMS language features in student written samples, in particular, have focussed mainly on the SMS language, or the types of SMS language features present in student writing, without also focussing on the morphosyntactic structures of such SMS language. The current study attempts to examine both the morphosyntactic structures of the SMS language used in the written samples of Communication English I students, and the types of SMS language features present in such written samples. When this study was conceptualised, it was felt that, in the South African context in particular, not much concurrent research work had been conducted into the twin areas of SMS language features in students’ text messages, and the morphosyntactic structures of the SMS language observed in students’ written work. The present study is of the view that there is a dearth of research in these twin areas of texting. As such, it is intended as a contribution to these two areas of students’ texting and formal writing.

Four of the studies that have investigated SMS language, or SMS language features, in students’ written work, and whose findings are worth highlighting are: Aziz et al. (2013), Freudenberg (2009), Mahmoud (2013), and Odey, Essoh and Endong (2014). For example, the study by Aziz et al. (2013) involved 50 undergraduate students at an Institute of Information Technology in Pakistan who were enrolled in two degree programmes (Bachelor of Computer Engineering and Bachelor of Telecommunication Engineering). Forty-two of these students were males, whilst eight of them were females, and their overall ages ranged from 19–25 years. A major finding of this study pertaining to student essays is that there was no significant prevalence of SMS features (e.g., abbreviations, emoticons, and omissions of punctuation marks) in these essays – which suggests that students were able to switch to an appropriate register or style when writing formally (Aziz et al.2013).

In a different, but related, context Freudenberg’s (2009) study set out to investigate the impact of SMS speak on the written school work of English first language (L1) and English second language (L2) high school learners. Undertaken at an English-Afrikaans dual-medium school in the Western Cape, South Africa, the study involved 88 learners – 43 from Grade 8 and 45 from Grade 11; 51 were English L1 speakers and 37 Afrikaans L1 speakers. Two instruments, questionnaires and a written English task, were used to collect the data. Questionnaires were administered to determine the frequency and volume of participants’ use of SMS speak as well as the features of their SMS speak. Similarly, participants’ written English samples were intended to assess specific features of the learners’ SMS speak. Two of the findings of this study are worth mentioning. All participants reported using features of SMS speak in their SMSes, and many reported using SMS speak in their written school work. But in contrast, samples of their written work did not contain a great number of instances of SMS speak features (Freudenberg 2009).

For its part, Mahmoud’s (2013) study examined the effect of English SMS language on the development of 40 Foundation Year students’ speaking and writing skills at a university in Saudi Arabia. The study took place over six weeks in the academic year 2012–2013. A research question the study set out to answer was: does the frequent use of SMS affect students’ spoken and written communication skills? The 40 participants were randomly assigned to two groups: a control group and an experimental group, each consisting of 20 students. The control group was taught using conventional strategies, whilst the experimental group was taught using both conventional strategies and SMS messages as an additional communication means. Three instruments were employed to collect data: SMS messages written in full English words and which were free of short forms and abbreviations; an oral test consisting of two tasks; and, a written test in which participants were asked to write a well-organised paragraph about one of the two topics related to a Foundation module they were being taught. One of the findings of this study was that students who used SMS had their writing and speaking performance noticeably improved (Mahmoud 2013).

In another context, the study by Odey et al. (2014) explored the influence of SMS texting on the writing skills of students at a college of education in Nigeria. These students, who served as the participants for the study, were 50 third year students. Using both quantitative and qualitative approaches, the study collected its data through 250 sample SMS texts produced by the students, 50 student essay scripts, and observation. With reference to the 250 SMS messages, students were requested to forward five of the most recent SMS texts they had sent to their friends, to the researchers who undertook this study. These SMS texts were analysed by identifying the SMS language features they displayed. The 50 essay responses were written by students as part of their examination and were content-analysed to establish the extent to which the SMS language features observed in the SMS texts occurred in them. The five most dominant features of the SMS language identified in student essay responses, in an exponential order, were: vowel deletion; graphemes (letter homophones); alphanumeric homophones; punctuation errors; and, initialisation (Odey et al.2014).

In relation to the morphosyntactic structure of student SMS language, Adebileje’s (2014) study explored the use of various registers in the syntax of text messaging amongst young undergraduate students at a university in Nigeria. Specifically, the study investigated the internal structures of words (morphology) and how words were put together to form text messages (syntax). Its corpus consisted of 120 text messages produced by students whose ages ranged from 16–24 years. The frequency and distribution of these text messages were analysed to establish how they differed in terms of register. The study discovered that students’ use of morphemes to construct syntax was mainly based on logograms, symbols (figures), phonics, Nigerian Pidgin English, and respective mother tongues.

In one more scenario, the study by Kahari et al. (2013) explored the syntactic structures of text messages in the English language used by 50 students in Zimbabwe. These students comprised 30 females and 20 males. They were requested to forward two messages each to the researcher. In the end, 90 text messages were isolated, and their sentences were analysed for aspects such as omissions of pronouns, auxiliary verbs, and contractions. In addition, the study analysed the impact of sociolinguistic variables on the English sentence structure of text messages. Moreover, unstructured interviews were conducted to find out what factors triggered the syntactic elements identified in the students’ text messages. The researchers point out that text messages showed that cell phone texting was affected by factors such as: channel constraints; time; linguistic pragmatic interference; common knowledge background; and, gender; and that these factors triggered the syntactic features identified in the students’ text messages (Kahari et al.2013).

Finally, Nweze’s (2013) study explored the morphosyntactic aspects of SMS texting amongst Global System of Mobile (GSM) communication users, all of whom were students at a university in Nigeria. In all, there were 50 such users (males and females), and 75 text messages were sourced from them. Mostly, these messages comprised educational, seasonal, love, religious, and other messages that expressed good wishes. The study employed transformational and meta-pragmatic theories to mount its data analysis. It then discovered that there were morphosyntactic variations amongst texters which violated formal English. It also found that texters employed some word order (which deviated from formal English), and that morphological processes such as contractions, abbreviations, acronyms, compounding, and blends featured in varying degrees in the texters’ messages (Nweze 2013).

The study adopted a qualitative research paradigm. Accordingly, it employed an interpretivist approach. The choice of both the research paradigm and the research approach was informed by the data types collected: text-based SMS messages and short written paragraph responses. In line with this research paradigm, the research design deemed appropriate for this study was an explanatory and case study research design (Creswell 2013; Henning, Van Rensburg & Smit 2004; Yin 2003). This is more so, since the study focused on participants at a case study level and analysed its data through a descriptive framework.

There were four research questions for this study:

  • What are the morphosyntactic structures of the SMS language used by Communication English I students?
  • Do the syntactic structures used by the students in their text messages conform to, or deviate from, Standard English syntactic structures?
  • What are the types of SMS language features these students use in their written work?
  • What is the frequency of textisms in these students’ writing samples?

Utilising an explanatory design (Creswell 2013; Yin 2003), this study had 90 undergraduate students enrolled in a national diploma module, Communication English I, at a university of technology in Gauteng as its participants. Communication English I is a one-year undergraduate module spanning two semesters and is offered to First Year national diploma students at this particular university of technology in Gauteng. The 90 participants were randomly selected during the first academic semester in 2013. They consisted of 50 females and 40 males with ages ranging from 19–22 years (mean age = 20.7 years). They all spoke English as a second language, whilst having one of the five black South African languages as their home language.

Two types of data were used for this study. The first type of data was sourced from participants’ mobile phone text messages. Participants were asked to forward two text messages they had sent to their friends in the two previous days to two research assistants’ mobile phone handsets (the authors understand that this procedure could constitute a possible limitation to the conclusions to be drawn from the study findings, since the participants may have selected their most grammatically-correct SMSes, in spite of the fact they were advised beforehand that this was not an exercise in good grammar). They were requested to transcribe one text message verbatim and to electronically transfer the other one in its original form to the two assistant researchers’ mobile phone handsets. In all, 180 text messages were collected from the participants. The first set of transcribed text messages had a total word count of 7100 words (average word count = 79 words). In contrast, the second set of electronically transferred text messages had a total word count of 5420 words (average word count = 60 words). The second type of data were writing samples sourced from the students’ short written essay task. For this essay task, they were requested to write a short essay on the following topic: What made you choose to study for a Diploma in Office Management? Participants were informed, prior to writing this essay task, that their (hand-) written essays would be marked and graded following the conventions of Standard English. No dictionaries or spell-checkers were made available to them. They were also informed that they needed to spend only 20 minutes writing this task (a time limit was imposed to reduce the inconvenience to the participants, whilst ensuring that the authors had enough data to analyse). The total word count for the combined short written essay tasks was 8038 words (average word count = 89 words).

Data analytic procedure – Morphosyntactic analysis

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The analytic procedure used to analyse the two data types for this study was a morphosyntactic analysis informed by content analysis (see Odey et al.2014). This analytic procedure entailed analysing each data type at both morphological and syntactic levels. In relation to participants’ text messages especially, the analysis focused on the morphological processes at play at the lexical level within the text messages and how words were structured at the syntactic level to communicate text messages at the sentence level (see Adebileje 2014; Nweze 2013; Odey et al.2014). The morphological processes that served as units of analysis were: contractions; shortenings and abbreviations; initialisms and alphabetisms; aphaeresis; phonetic approximations; G-clippings; rebus, letter and number or number and letter homophones; accent stylisations and respellings; misspellings and typos; omissions; upper and lower cases; logographs and emoticons; and, combined two words. These morphological processes are referred to here as textisms, following Powell and Dixon (2011), Varnhagen et al. (2010), and Wood, Kemp and Waldron (2014). At the syntactic level, units of analysis were: word order (e.g., the subject-verb-object [SVO] word order); full sentences; sentence fragments; run-on sentences; subject-verb agreement (SVA); punctuation marks; unconventional punctuation marks; no punctuation marks; and, colloquialisms. All of these syntactic structures of the participants’ text messages were analysed in terms of whether they conformed to, or deviated from, Standard English syntactic structures.

In respect of the participants’ writing samples, the analytic procedure examined which text message forms or features, if any, were incorporated into their written work (Adebileje 2014; Kahari et al.2013; Nweze 2013; Oladoye 2011). No t-test was administered. However, two coders content-analysed each piece of data type for the presence of the units of analysis indicated above. Their intercoder percentage agreement was .90 and .92 for the morphological features (textisms) found in the two sets of text messages (see Tables 1 and 2), respectively, and .88 for the syntactic structures found in both text message types (see Table 3). With regard to textisms identified in participants’ writing samples, the inter-coder percentage agreement was .92 (in this case an inter-coder agreement of 1.00 represents total agreement, whilst an inter-coder agreement of .00 represents zero agreement on the part of coders). Where necessary, different SMS language features were categorised, represented in their occurrence frequency percentages, and tabulated accordingly.

This section presents the findings related to the two data types cited above. As such, these findings are largely specific and responsive to the nature of the data types as sourced from the participants’ text messages and writing samples.

Morphological processes at play in participants’ text messages

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Participants contributed two text messages each in response to a request to do so. These text messages were grouped into two sets: the first set consisted of text messages transcribed by the participants, whilst the second set comprised text messages electronically transferred by them and recorded on the two mobile phone handsets used in the study. Two of the transcribed instances of the first set of text messages (TM) are represented here as TMA and TMB.

TMA
  1. Um havin da best I thanx u 2 hv a very gudnyt!
  2. Thinking of u. hop u’r suprb, hv a gr 8 1!
  3. Baby I hope u hv a gud nyte. Lv u 4eva
  4. Baby I hope u’l b wel.
  5. Thanx my dear luving sister 4 this msg. phone u 2moro 4 visit
  6. mI am da last person 2 wish u happy birthday- HAPPY BELATED BIRTHDAY.
TMB
  1. Hi ma day was gr8 l went 2 church n after I went loftus 2 watch soccer, in da campus
  2. Hey I hope u had a grt day, ern nw hav a grt nyt gudnyt
  3. Bby I just got home!come!
  4. Frm 1yrz to 15yrz I hv a heart 4 childrens, Im doing local government
  5. It ws so nyc, it ws jc a visit dat we usual pay 2 da kidz by showing luv 2 them
  6. Hud it sakhile da guy hu took u number ma number r 073 847 3893
  7. Karabo cn u plz sign 4 me ko APL, I wont manage 2 cum 2 skul 2day 4frm Dimakatso.
  8. My sweet pumkin, how r u my love? I miss u so… much and I angry coz u forgot my bday on da 11th …. Take care, have mad love 4u. I great week ahead.
  9. I at the gate plz open it
  10. Hi u, I miss u & so hw ws ur week. I wl like 2 sy I luv u

Table 1 displays participants’ transcribed SMS language features and a typology reflecting the corresponding textism categories into which these features have been divided. These textism categories represent morphological processes at play within participants’ text messages.

TABLE 1: Participants’ transcribed SMS language features and corresponding full versions.

As shown in Table 1, the students’ text messages contained twelve textism categories; the top five (with their corresponding occurrence frequency percentages), in a descending order of occurrence, being: initialisms and alphabetisms; rebus, letter and number or number and letter homophones; accent stylisations and respellings; phonetic approximations; and, misspellings and typos.

Likewise, instances of text messages electronically transferred by participants and recorded on the two mobile phone handsets used in the study are depicted in Figures 1 and Figure 2.

FIGURE 1: Sample of participants’ electronically transmitted SMS language features and corresponding full versions.

FIGURE 2: Sample of 2nd set of participants’ SMS language features and corresponding full Versions.

Table 2 (on electronically transferred SMSes) indicates 13 textism categories; the first eight in the following descending order of frequency occurrence being: initialisms and alphabetisms; phonetic approximations; misspellings and typos; accent stylisations and respellings; shortenings and abbreviations; rebus, letter and number or number and letter homophones; combining two words; and, upper and lower cases.

TABLE 2: Participants’ electronically transmitted SMS language features and corresponding full versions.

Syntactic processes at play in participants’ text messages

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Table 3 features syntactic processes found to have been at play in participants’ text messages. In particular, it displays the respective categories into which these processes were divided and whether or not participants’ text messages conformed to, or deviated from, these syntactic categories. For instance, in respect of the word order a majority of text messages displayed the conventional SVO word order. Similarly, in relation to full sentences, a majority of text messages were full sentences. In contrast, fewer text messages were sentence fragments.

TABLE 3: Syntactic categories and instances of participants’ text messages that fit into these categories.

With reference to run-on sentences, too, there were fewer text messages that were run-on sentences. Concerning the subject-verb agreement (SVA) structure, all text messages conformed to this structure. As regards punctuation marks, some text messages employed punctuation marks, such as periods (full stops) and commas properly, whilst others did not. Furthermore, a majority of text messages did not have unconventional punctuation marks, except a few that used an ampersand, &, in the place of and, whilst few used emoticons such as ;) and ({}) at their sentence endings. Finally, one third of text messages did not have periods at sentence endings. On this score, seven text messages utilised the colloquialism, wana or wanna, for want to.

Type of SMS language features in participants’ writing samples

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As mentioned earlier on, with regard to participants’ writing samples the analysis determined which text message forms or features, if any, they incorporated into their written work. Table 4 represents a typology of SMS language features (including their corresponding occurrence frequency percentages in brackets) and the accompanying examples of such features from participants’ short essay writing samples. In this case, the four SMS language features that occurred mostly in participants’ writing samples were, in a descending order: phonetic approximations (1.68%); misspellings and typos (1.30%); shortenings and abbreviations (1.22%); and rebus, letter and number or number and letter homophones (1.1%).

TABLE 4: A typology of SMS language features and corresponding examples of such features in participants’ writing samples.

The eight SMS language features that appeared less in participants’ writing samples were, in a descending order: accent stylisations and respellings (0.92%); initialisms and alphabetisms (0.84%); upper and lower cases (0.84%); contractions (0.46%); aphaeresis (0.38%); apostrophe omissions (0.30%); combined two words (0.15%); and colloquialisms (0.1%). Two categories, G-clippings and logograms and emoticons, were not detected.

This study set out to explore the morphosyntactic structures of the SMS language of Communication English I students, and the types of SMS language features these students used in their written work at a university of technology in South Africa. Below is a discussion of its findings as they relate to the three areas analysed in the preceding section.

Participants’ text messages, and their morphological structures

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As mentioned earlier on under the findings section, the morphological structures detected in the participants’ text messages in the first set of data were categorised into thirteen textisms. As depicted in Table 1, the morphological structures of the textisms used in this set of participants’ text messages, had, as is the case with most SMS textisms (Powell & Dixon 2011; Varnhagen et al.2010; Wood et al.2014) their own distinctive features which deviated from those used in formal English. Pathan (2012) makes a similar observation in his study of 200 text messages generated by Bachelor of Arts (English) students at a university in Libya.

In this regard, the two textisms with the highest occurrence frequency percentages were rebus, letter and number or number and letter homophones, and initialisms and alphabetisms. These occurrence frequency percentages seem to be slightly lower than the highest occurrence frequency percentages of textisms reported in other studies on text message features, such as those studied by Lyddy et al. (2013) and Thurlow and Brown (2003). In addition, the occurrence frequency percentages for the individual textisms, as depicted in Table 1, are lower than those reported by both Lyddy et al. (2013) and Thurlow and Brown (2003). For instance, in the study by Lyddy et al. (2013), textisms such as missed capital letters, accent stylisations, and omitted mid-message punctuation (mainly apostrophes) were scored as 22%, 19%, and 11%, apiece. Moreover, the proportion of non-standard spelling in their study was 19%. In contrast, as reflected in Table 1, the apostrophe omissions in the first set of text messages was 2.53%, as compared with 11% of that by Lyddy et al. (2013) for this textism category. Furthermore, the average textism occurrence frequency percentage (5.3%) in the first data set compares quite unfavourably with Plester, Wood and Joshi’s (2009) study, in which textisms accounted for 34% of the total text message content. On the other hand, in Thurlow and Brown’s (2003) study, textisms accounted for 20% of the total text message content. However, here too the morphological structures of the textisms detected in participants’ text messages in the first data set deviated from those applicable to formal English – a point that Pathan (2012) notes in his study as well.

Again, as illustrated in Table 2, the morphological structures analysed in the participants’ text messages in the second data set were also categorised into thirteen textisms. As is the case with textisms used in the first data set, the morphological structures of the textisms used in this set of the participants’ text messages deviated from those applicable to formal English. Nonetheless, in this data set, the two textisms with the highest occurrence frequency percentages, initialisms and alphabetisms and phonetic approximations, recorded higher occurrence frequency percentages than those in the first data set. These occurrence frequency percentages seem to be higher than those reported in other studies on text message features, such as the studies by Lyddy et al. (2013) and Thurlow and Brown (2003). They are also marginally higher than the textism of 34% in Plester et al. (2009). However, the apostrophe omissions for this data set are, at 2%, lower than 11% of Lyddy et al. (2013) for the same textism category. Above all, as is the case with the first data set, the average textism occurrence frequency percentage (13.5%) in the second data set compares quite unfavourably with the study by Plester et al. (2009), in which textisms accounted for 34% of the total text message content.

Participants’ text messages and their syntactic structures

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There were nine syntactic categories (Table 3) that were employed to analyse the participants’ text messages with a view to establishing whether such text messages conformed to, or deviated from, the syntactic categories in question. For example, as regards the SVO word order, a majority of text messages displayed conventional English SVO word order. This observation is inconsistent with Nweze’s (2013) study that notes that texters’ word-ordering in his study deviated from formal English. It is also incongruent with Pathan’s (2012) study that found texters’ messages to have been characterised by sentence subject omissions (see Chiad 2008). With reference to full sentences, a majority of text messages were full sentences. In contrast, fewer text messages were sentence fragments. Similarly, in respect of run-on sentences, there were a few text messages that were run-on sentences. Again, this observation contrasts with Pathan’s (2012) study that found texters’ messages to have been typified by, for example, run-on sentences.

Pertaining to the subject-verb agreement (SVA) structure, all text messages conformed to this structure. As regards punctuation marks, some text messages utilised punctuation marks, such as periods (full stops) and commas, at appropriate sentence slots, whilst others did not. Moreover, a majority of text messages did not employ unconventional punctuation marks, except a few that used an ampersand, &, for and, whilst a few used emoticons such as ;) and ({}) at their sentence endings. Finally, seven text messages made use of the colloquialism, wana or wanna, in lieu of want to.

Participants’ writing samples and types of SMS language features

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As shown in Table 4, the four SMS language features that had a high occurrence frequency in participants’ writing samples were: phonetic approximations; misspellings and typos; shortenings and abbreviations; and rebus, letter and number or number and letter homophones. With the exception of misspellings and typos, this finding differs with Freudenberg’s (2009) study of SMS language features in student writing samples. That is, in Freudenberg's study, spelling errors, over-punctuation, lack of punctuation, and omission of function words had a higher occurrence frequency. Again, as depicted in Table 4, the other SMS language features such as accent stylisations and respellings, initialisms and alphabetisms, upper and lower cases, contractions, aphaeresis, apostrophe omissions, and colloquialisms occurred less frequently. This observation, save for upper and lower cases, aphaeresis, and apostrophe omissions is in line with Freudenberg’s (2009) study, in which abbreviations and acronyms, shortened words, and colloquialisms occurred less frequently in student writing samples. However, the frequency and proportion of textisms in participants’ writing samples, as illustrated in Table 4, is lower than that reported in studies such as Freudenberg (2009), and in Rosen et al. (2010). This is consistent with the finding of Aziz et al. (2013) in their participants’ written work.

Whilst it would be interesting to attempt explanations of why these data differ from those in the literature reviewed earlier, and how the findings could be applied to the teaching and learning of writing (English Additional Language), this study was, first and foremost, descriptive and exploratory in nature. In addition, the findings of the study satisfy the research questions the authors set out to explore.

Conclusions and recommendations for further studies

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This study set out to investigate the morphosyntactic structures of the SMS language of English Communication I students and the types of SMS language features these students used in their written work at a university of technology in South Africa. With reference to the morphological structures of SMS language, it was discovered that, in one instance, the occurrence frequency percentages of certain textisms (e.g., rebus, letter and number or number and letter homophones, and initialisms and alphabetisms) in the participants’ text messages, were slightly lower than the highest occurrence frequency percentages of textisms reported in other studies on text message features, such as Lyddy et al. (2013) and Thurlow and Brown (2003). In another instance, it was found that two textisms (e.g., initialisms and alphabetisms, and phonetic approximations) with the highest occurrence frequency percentages in the second data set, yielded higher occurrence frequency percentages than those in the first data set. It also emerged that the occurrence frequency percentages of these two textisms seemed to be higher than those reported in other studies on text message features, such as in Lyddy et al. (2013) and Thurlow and Brown (2003), and marginally higher than in Plester et al. (2009) - textism of 34%. However, it was noted that the apostrophe omissions for this data set were, at 2%, lower than that of Lyddy et al. (2013) of 11% for this textism category.

It also emerged that the participants’ SMS language features – like those reported by Pathan (2012) – deviated from those used in Standard English. In respect of the syntactic structures of the participants’ SMS language, it was observed that a majority of participants’ text messages employed the conventional English SVO word order and the accepted SVA structure. The same was the case with participants’ text messages in relation to full sentences: a majority of their text messages were full sentences, with only a few of such text messages being fragments, or run-on sentences. Furthermore, it was observed that the participants employed punctuation marks such as periods and commas varyingly, with some employing them appropriately, whilst others did not. Moreover, it was noted that few participants used an ampersand, &, for and, whilst a few others used emoticons such as ;) and ({}).

With regard to the participants’ writing samples, four SMS language features occurred frequently: phonetic approximations; misspellings and typos; shortenings and abbreviations; and, rebus, letter and number or number and letter homophones. In contrast, SMS language features, such as accent stylisations and respellings, initialisms and alphabetisms, upper and lower cases, contractions, aphaeresis, apostrophe omissions, and colloquialisms occurred less frequently. Most importantly, the frequency and proportion of textisms in the participants’ writing samples were lower than those reported in studies, such as Freudenberg’s (2009) and that by Rosen et al. (2010). Finally, the findings of this study are largely specific and responsive to the nature of the data types, as sourced from the participants’ text messages and writing samples. As such, there are studies that may replicate these findings, and those that may not. Moreover, cross-sectional studies involving students across study levels and involving a lot more triangulated data types are needed to better understand the morphosyntactic forms employed by students in their SMS language.


Competing interests

The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.

Authors’ contributions

Data for this article were collected by M.L.M. (Tshwane University of Technology) from a university of technology. C.C. (Tshwane University of Technology) wrote the first draft, and C.C.M. (Tshwane University of Technology) fine-tuned the final draft.

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