A Sociolinguistic Analysis of the Use of Arabizi in Social Media Among Saudi Arabians
Bibliographic record
Abstract
The aim of this sociolinguistically-oriented study is to explore the Arabizi phenomenon which is characterized by spelling Arabic words using the Latin script. It is prevalent in the text-based computer-mediated communications among Saudi Arabians. The study focuses on why Arabizi is used, how, particularly in respect to with whom and in which topics, it is used, the attitudes of its users toward its use and the perceived advantages and disadvantages of its use. Using an online survey, data were collected from 241 participants, 72 of which were users of Arabizi. The findings revealed that the primary reasons for using Arabizi were its being a communication code among youths and a compensation for the lack of Arabic keyboard from technological devices as well as being more expressive than Arabic language. It was also found that Arabizi was primarily used to communicate with friends and individuals of the same age, but not with parents and older people or in formal relationships. In addition, the study revealed that Arabizi is used in occasional conversations and social matters, but not in academic, scientific, business, economic, religious or poetry and literature- related topics. Arabizi users were found to hold both positive and negative attitudes based on different advantages and disadvantages of the phenomenon. These findings will be discussed and recommendations for future research will be given.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".