Mobile phone text messaging language : how and why undergraduates use textisms
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».