2. Data generation

Using as a convenience sample, a first-year Language and Communication class I teach at Cardiff university was asked towards the end of one lecture to retrieve from their phones five messages that they had either sent or received in the previous week and to transcribe them as accurately as possible (i.e. ‘exactly as they appeared on the display screen’). This was done as part of a larger questionnaire study being conducted by Brown (2002) also examining patterns of SMS use (e.g. reasons for using it, people they send messages to, and whether or not they use ‘predictive text’) and other practical considerations such as the amount of money spent on text-messaging, the person who pays the bills, and the network used. Participants were assured of the confidentiality and anonymity of their responses; this was especially important given the personal nature of the messages.

Of the students available, 135 (aproximately 70%) of them responed to this request. The mean age of participants was 19, with three-quarters of them female students (n = 120, 75%) and a quarter of them male students (n = 39, 25%). As is typical of the university’s intake more generally, with 28% of the students actually from Wales itself, almost all the participants were British (98%). While Cardiff University attracts a largely White, generally middle-class population of students, there is no apparent reason why this sample might not otherwise be fairly representative of young, university-age adults in Britain as a whole. However, even though anecdotal evidence suggests that many other young people their age are equally heavy users of mobile phones and text-messaging,I obviously cannot assume that the sample is more widely representative in terms of educational background and socio-economic status.

A total of 544 separate messages were recorded by participants which were transcribed as accurately as possible into a single electronic document. Each message was then analysed in terms of a number of central interests: (a) length (i.e. number of words/characters used); (b) main typographical and linguistic content (e.g. emoticons, abbreviations and letter homophones), and, in line with standard Content Analytic procedures (cf. Bauer, 2000), (c) thematic priority/primary function. At this stage, and partly given the size of the sample, it has not been necessary to undertake elaborate statistical analyses other than to calculate descriptive tendencies in terms of message length.