Medium Effects

As for medium, textisms of many forms, operations, and functions occurred most in MSN, for the age groups taken together. In other words, the medium of MSN chats deviates the most from Standard Dutch orthography. Although the present corpus

study does not allow us to pinpoint the reasons with any certainty, this is probably due to the characteristics of the new media, as presented in Table 14 below (see also Verheijen, 2015) – synchronicity, visibility, level of interactivity, and technology. Such factors were also recognized by De Decker (2015) and Hilte et al. (2016), who found a similarly large impact of medium in Flemish written CMC. Like WhatsApp, MSN is a (near-)synchronous medium: in instant messaging, communication takes place almost in real time, approaching a spoken conversation. This stimulates users to respond rapidly. In the often fast-paced chats on MSN (and WhatsApp), adhering to the standard orthography is likely to be of less importance than replying quickly, which would lead to more deviations from the standard language – even, for example, to more textisms of omission than in SMS and on Twitter, despite their message size restrictions. Deviations which, moreover, need not be avoided for fear of being criticized by many people, since MSN is a private medium. Messages are sent to selected recipients (‘one-to-one’ or ‘some-to-some’), often family and friends, who are presumably less critical of their interlocutor’s deviating orthography in order not to damage their relationship, since criticism on spelling can be considered a face- threatening act and thus be harmful to interpersonal relationships, according to face negotiation theory (Ting-Toomey & Kurogi, 1998). Furthermore, the higher relative frequencies of textisms in MSN chats may result from the complete lack of predictive software that was available on computers, whereas communication via the other three media usually takes place via mobile phones with predictive dictionaries as the default setting – dictionaries that, of course, adhere to the standard language orthography.

Table 14. Variables of four new media.

Characteristic Options MSN SMS Twitter WhatsApp

Message size

limit yes (max. no of characters)  (160) i  (140 > ₂₈₀₎

no  

Synchronicity of

communication (near) synchronous  

asynchronous   Visibility public  private   () ii _ Level of interactivity one-to-one   () ii _ one-to-many () iii _ some-to-some  iv _ iv

Technology mobile phone   

computer  ()  ()

i _{Except for concatenated text messages: messages linked together when the limit is} exceeded. ii _{direct message.} iii _{broadcast message.} iv _{group chat.}

The only textisms that did not occur most frequently in MSN chats were for understandability or with punctuation. Yet textisms with punctuation were only just more frequent in SMS than in MSN (81.86 vs. 80.44), so the difference was minor.

This leaves us to explain the frequency of textisms for understandability (i.e. with extra hyphens or extra spacing), which were more frequent in WhatsApp than MSN (57.25 vs. 31.36). A first reason for this might be that the WhatsApp data were collected some years later than the other texts: perhaps the influence of the English language and its spacing conventions for compound words has grown in the Dutch language (or, at least, in Dutch online youth language), even in this short time span. This redundant use of spaces is a salient element of what has pejoratively been called the “English disease,” i.e. the increasing visibility of the English language in the Dutch language (Dings, 2010; Vandekerckhove & Sandra, 2016). The second possible reason has to do with technology. WhatsApp is mostly used on mobile phones, which now often have a predictive dictionary as the default: this tends to introduce extra spacing within compound words, while MSN was used on personal computers, for which predictive dictionaries were not available.

When we take the two age groups together, textisms of eight of the thirteen forms, operations, and functions occurred least on Twitter, while textisms of four of those thirteen occurred least in SMS text messages. Of the media that were studied, tweets thus contain the least deviations from Standard Dutch orthography. This even goes for textisms of omission and for reduction, notwithstanding the strict message size limit which was then set at 140 characters for tweets, which requires communicating economically. The lower relative frequencies of textisms on Twitter may have to do with other characteristics of this medium, as shown in Table 14 – visibility, level of interactivity, and synchronicity. Communication in Twitter is typically public and ‘one-to-many’, so tweets can be read by a large audience, either all tweeters or all of one’s followers. This might discourage users to deviate greatly from the standard language in their tweets. This microblogging platform is a favourite stomping ground for language prescriptivists to vent their feelings on grammar or spelling ‘errors’ – in fact, for people to complain in general, not just about perceived language deterioration, but also, for example, about companies and politicians through negative electronic word-of-mouth (NeWOM) or negative indignation (Pfeffer, Zorbach, & Carley, 2014). The following tweets from my corpus (1)–(2) contain explicit disapproval of spelling deviations from Standard Dutch (with censure of criterea instead of criteria, and locatie with a k instead of a c):

(1) Als universiteit een promotievideo voor je applicatie maken en dan consequent criteria als criterea spellen. #studietimer #owd11

(‘When a university makes a promotional video for their application and then consistently spells criteria as criterea. #studietimer #owd11’)

(2) Zeg #POWNEWS. Is het niet locatie. Met een C (‘Hey #POWNEWS. Isn’t it location. With a C’)

This contrasts with the privacy of text and instant messages, which are sent between two people (one-to-one) or among a limited group of people (some-to-some): the smaller size of the possible audience in these media decreases the chances of language criticism and might make CMC users less hesitant to deviate from the standard language norms. In fact, such private media may even stimulate the creation of a

group language which differs from the standard language, to enhance social bonds and in-group affiliation (Zappavigna, 2012). The low occurrence of textisms on Twitter and in SMS might also be attributed to their asynchronous nature. Communication takes place in ‘deferred time’, making these media less conversational: replies to text messages and tweets are often only sent after some time has passed. This gives users an opportunity to unhurriedly edit their orthography and filter out any possibly unwanted textisms, although of course it remains conjecture whether users indeed make use of this opportunity.

Although they were also infrequent on Twitter, there was one form of textism that was not least frequent on Twitter or SMS, but on WhatsApp: those with punctuation. This class contains missing (apostrophes, periods except sentence-final, hyphens), reduplicated (exclamation marks, question marks, periods), and extra (hyphens) punctuation. Detailed scrutiny of the data reveals that this low frequency in WhatsApp is not because of fewer missing punctuation marks, but because of fewer reduplicated punctuation marks: periods, question marks, and especially exclamation marks are less often repeated in this medium, as compared to the other media. Again, we can only speculate as to why this is the case, but what may be relevant here is the rise of emoji between the 2009-2011 and 2015 collection periods (and afterwards). These ideograms have become very popular and are used in various ways, such as to visually enrich typed text and to convey emotions (Danesi, 2017). Both emoji and punctuation can compensate for a lack of paralinguistic cues like stress and intonation (Evans, 2017) – elements that are present in speech, but absent in writing. Emoji can make digital communication more expressive (Novak, Smailovic, & Mozetič, 2015), as repeated punctuation can, so they meet similar purposes. Reduplication of punctuation might thus occur less frequently in the WhatsApp data than in the data from the other media, collected earlier, because emoji are booming.

6. Conclusions

This paper has presented a corpus study of nearly 400,000 words of Dutch youths’ written computer-mediated communication, covering four new media (MSN chat, SMS text messaging, Twitter, and WhatsApp) and two age groups (adolescents, 12- 17 years, and young adults, 18-23). The analysis of textisms in this new media corpus makes clear that the orthographic deviations in Dutch youths’ CMC, similar to those in CMC in other languages, are not random ‘violations’ of the Standard Dutch orthography. Though they might not be aware of it, youths use textisms of specific forms, with specific edit operations, and for specific functions. Dutch CMC language is implicitly governed by orthographic principles, so to regard textisms simply as pointless orthographic ‘errors’ is a short-sighted view.

These principles have been uncovered by classifying the textism types in terms of forms (letters, diacritics, punctuation, spacing, capitalisation) and edit operations (omission, substitution, addition), but also, importantly, functions. Five functions of textisms have been identified, the ‘CC5-functions’ or ‘SUPER-functions’: they can make orthography more Speechlike (for Casualness and Colloquialism),

Understandable (for Clarity and Comprehension), Playful (for Creativity and Coolness), Expressive (for Compensation and Cues), or Reduced (for Conciseness and Curtailment).

Analysis of the relative frequencies in the corpus of these classes of textism types reveals how textisms are used effectively by Dutch youths. They have been shown to most frequently omit letters to achieve orthographic brevity and velocity. In Dutch youths’ written CMC, the most important of the SUPER-functions is thus reduction of the keystroke effort. The aforementioned Het Groene Boekje, a benchmark for Standard Dutch spelling, has the (debatable) slogan “Correct language is vital for successful communication.” This paper has shown that where new media writings are concerned, a variation on this theme is, in fact, true. ‘Correct’ or standard language is not vital for successful computer-mediated communication. On the contrary, although the majority of spellings still conform to the standard orthography, it is ‘incorrect’ or non-standard language that has been proven to be vital for effective written computer-mediated communication among youths.

In addition, this study has shown that the writer’s age group and the text’s medium significantly affect the relative frequencies with which textism types of different classes are used in Dutch youths’ new media writings. Textisms were overall used much more by adolescents and in MSN chats, followed by WhatsApp messages, whereas they were used less by young adults and in SMS and particularly tweets. Each medium has its own unique combination of characteristics and constraints, while adolescents and young adults to a certain extent reveal differing perceptions on the importance of standard language and orthography. MSN is a near-synchronous, private, one-to-one or some-to-some, computer-based medium; Twitter, by contrast, is a public, one-to-many, asynchronous medium. Adolescents are quite non- conformist in their language use in CMC; young adults are somewhat more conventional in comparison.

The present study proves that Dutch youths pragmatically use textisms of various types as orthographic adaptations to deal with the possibilities and confines of different new media, as well as with the discursive demands of computer-mediated communication within their age group.