Considering the limitations of this study, it has to be noted that the functional classification of textisms is somewhat problematic, since a corpus study cannot, of course, provide conclusive evidence for why people use certain textisms. Yet asking them what their reasons are would be unreliable, because people may not be fully aware why they use a specific orthographic form in a specific context. The approach taken in the present study is preferable, as it is more objective than self-assessment. Nevertheless, we cannot be sure about writers’ intentions: for example, though it is assumed that abbreviations such as ‘initialisms’ are generally used because they are quicker to type or help to keep a message within a character limit, some youths might use them to make their message more casual. A single textism can thus be used for multiple purposes, so the five SUPER-functions are not mutually exclusive. An
additional function of textisms, not part of the classification applied here, is that all textisms may be used to conform to in-group usage norms, and to deliberately deviate from the standard language norms to help shape youths’ identity. However, this cannot be determined based on written output. To keep the analysis as objective as possible, all textism types have been assigned to the category/ies that was/were most evident on the basis of their form.
Another limitation of the present study is that although the demographic variable of age was included in the analysis, other social variables such as contributors’ educational background and gender were not included or controlled for. Unfortunately, this was impossible in the current study, since the education and gender of contributors to the (already existing) SoNaR corpus were unknown. These demographic variables were only known for the WhatsApp data, specifically gathered for the present study, which was insufficient to include them in the analyses. Significant linguistic differences in written CMC due to gender and education have been found in other studies (e.g. Baron, 2004; Schwartz et al., 2013; Hilte et al., 2016, 2017), so these variables deserve further exploration in future corpus-linguistic research.
Other potential variables that could be explored, if such data were known, include the interlocutor (their profile and users’ relationship with them), the conversational topic and goal, as well as any software (e.g. autocorrection) used in producing the CMC. Adding such variables would allow us to more fully explore the language variability in youths’ online messages.
Furthermore, some medium characteristics could be operationalized differently in future studies. In the present study, contrasts were posited in technology between written CMC on mobile phones and computers, as well as between synchronous and asynchronous CMC – distinctions that were still very much relevant for the time the data from the first three media were collected (2009- 2011). Yet recent developments in the technological properties of smartphones, as well as youths’ increased continuous access to smartphones, have blurred the lines between text messaging and instant messaging or online chat, making such distinctions overly simplistic for future studies. Future research could, instead, focus on possible linguistic variation between WhatsApp messages produced via the mobile app and those produced via the web app ‘WhatsApp Web’, used on desktop/laptop computers.
A final drawback is the comparison of the four media, for three of which (MSN, SMS, Twitter – from the SoNaR corpus) slightly older data were used, collected in 2009-2011, while one (WhatsApp) represents more recent data, collected in 2015. These differences in collection period, although only a few years apart, limit the validity of the comparison. Considering that youth languages, including online youth language, are dynamic, it is possible that differences between the WhatsApp data and the other three media may not only be caused by medium characteristics and constraints, but also by longitudinal changes in Dutch youths’ written CMC. Further research is needed to compare how their CMC differs in concurrent, recent new media data. Yet this could not include MSN chat, SMS text messaging, or Twitter: while Dutch youths used all these media some years ago, the former no
longer exists, the second has become obsolete, and the latter is no longer popular among Dutch youths.
Future studies could include data from the currently popular social media Snapchat or Instagram (Van der Veer et al., 2018), although Snapchat data would be extremely difficult to collect due to its ephemeral nature (snaps and chats are by default “automatically deleted once they’ve been viewed or have expired,” Snap Inc., 2018) and finding sufficient textual Instagram data would prove a challenge since this medium focuses on sharing visual content (i.e. photos and videos), to which textual content can be added but is only of marginal importance (Instagram, Inc., 2018). It is even possible that in a few years from now, WhatsApp has become a superseded medium. The fast-moving nature of new media, rapidly replacing each other, ensures that we will not run out of possibilities for analysing online linguistic variation any time soon.
Despite these limitations, the current study has presented an original contribution to existing research on written CMC, through a thorough, illuminating analysis of the interplay between the forms, operations, and functions of textisms; the medium of the text; and the age group of the writer in Dutch youths’ new media writings.
Acknowledgements
Thanks are due to my PhD supervisors, Wilbert Spooren and Ans van Kemenade, as well as to the anonymous reviewers for their feedback on earlier drafts of this paper.