The Linguistic and Situational Features of WhatsApp Messages Among High School and University Canadian Students
Bibliographic record
Abstract
WhatsApp messages can be such a rich source for creative and spontaneous language geared toward more individual expression. WhatsApping provides us with a unique view into language and is an interesting prototype for thinking about language use, the various functions of this variety and how it is used to render different kinds of meanings. This study aims to explore the linguistic features of text messaging’s communicative intent, content and context. Selected samples of messages were drawn from a high school student population in Canada who provided a corpus of 100 different texts already sent and/or received for personal, educational and professional purposes. The collected data were analyzed using Biber and Conrad’s qualitative approach to register, genre, and style analysis. The result is that people use clipped sentences in a free flow of casual speech and slang. While certain abbreviations have come into such common use, to the point of becoming standard, a wide array of individualistic variance in terms of style and language usage has emerged. It is concluded that avid texters, while appearing to greatly deviate from more traditional, standard written English, are a rich source for studying creative and spontaneous language adaptation of register, genre and text according to context and text users.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".