An Analysis of the Language Usage of the Twitch TV Users in the Context of Turkish Education
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it