Mobile phone text messaging language : how and why undergraduates use textisms
Bibliographic record
Abstract
Mobile phone text messaging has continued to increase in popularity since its inception in 1992, but research into the language used in text messages has produced variable results. The overall purpose of this thesis was to investigate factors which might be associated with variations in textism use between individual phone users. In previous research, methodological variations between studies have made comparisons difficult and include the use of various message collection methods (e.g., asking participants to create messages versus to provide previously sent messages) and variations in the definition, categorisation and counting of altered words in text messages, or textisms‚ÄövÑvp (e.g., 2nite for tonight). In Study 1 of this thesis, undergraduates (155 in Canada, 86 in Australia) were asked to provide text messages via three different collection methods. Messages that were translated and elicited under experimental conditions were found to contain more textisms than naturalistic messages copied from phones. Further, Australian participants used more contractive textisms (e.g., fri for Friday, bday for birthday) than Canadians, and more textisms overall. In Study 2, naturalistic data were collected from a further 386 Australian first-year undergraduates between 2009 and 2012. Over these time-points, textism use decreased, particularly for contractive textisms. Females used more expressive textism types (e.g., pleeease!?! for please) than males. Further differences in textism use were found to be related to the technology on participants' phones and to participants' attitudes towards textism use. In Study 3, the Australian and Canadian undergraduates from Study 1 completed a range of literacy and language tasks. The very few correlations between task scores and textism use that reached statistical significance were negative (students with higher linguistic scores used fewer textisms), although this relationship may have been influenced by differences in attitude and early literacy experience. In Study 4, the Australian students of Study 3 were able to discern situations in which textism use is appropriate. Further, the examination of 303 written exams of a separate group of Australian undergraduates confirmed that textisms were avoided in these students' formal writing. In conclusion, individual textism use in messages is related to a number of factors, especially the technology on mobile phones. Rather than being associated with poor literacy skills, textism use can be conceptualised as a form of literacy skill that is adapted to the social expectations of undergraduates and the developing technology on phones to produce maximally efficient and expressive text-based communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".