An Analysis of the Language Usage of the Twitch TV Users in the Context of Turkish Education
Bibliographic record
Abstract
The aim of this research is to examine the language usage of Twitch tv users in the context of Turkish education. The data of the study, which is descriptive qualitative research, were collected from the chat message of three streamers who produced the most watched Turkish programs aired on Twitch tv. The number of viewer messages analysed in the study is 32.764. The findings show that these messages are produced mostly in Turkish language, but there are also others produced in other languages. The messages are found to contain emotes, abbreviations, neologisms and random laugh expressions which are used for communicative purposes. Turkish expressions are used more in the chat broadcast whereas in the game broadcast foreign origin words are more frequently used. In addition, when the chat messages of the three streamers for the same game were examined, differences are found in the language usage of the viewers. In the use of emotes, abbreviations, neologisms and random laugh expressions, there is no difference from the language used by the streamer or in the content. The analysis shows that the use of these linguistic features changes depending on the context. When we examine the data in the context of Turkish education, it has been determined that the language used in the platform does not match the aims of the Turkish Course Curriculum (2019). In addition, it was determined that emotes, abbreviations, neologisms and random laugh expressions were an essential part of communication. It has been suggested that some language elements that young people use frequently in the digital environment and that contribute to the meaning should be included in the Turkish teaching process.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".