Language Visibility and Audibility: Discussing the Dominant Status of Yoruba on Social Media
Bibliographic record
Abstract
In recent times, there is evidence of the emergence of new linguistic dynamics in the social media communication engagements in the Nigerian social media culture which have consequently impacted the visibility of the Yoruba language. The use of Yoruba has become part of a lot of users’ everyday social communication practices thereby promoting the language to be more visible in the arena of social media platforms. This study is interested in evaluating the nature of and the extent to which the language is used on social media, understanding its presence to the development of social media repertoire, and how it has become the dominant local medium through which many Nigerian social media users negotiate and express their identities. The motivation for this practice, and how it is employed as a discoursal means of language promotion will also be investigated. The data contain Instagram comments that exhibit pure Yoruba and code mixing between Yoruba and English/Nigerian Pidgin English; and from the data, it is evident that Yoruba is gaining more popularity on social media networks amidst the dense multilingualism of Nigeria. The findings reveal that social media provide a discursive platform for the users to be able to reinforce dominant representation of the language. The paper concludes that Yoruba is emerging as a popular language of the Nigerian internet culture.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".