The Gag City Grammar Police: Language and Algorithmic Community on Stan Twitter
Bibliographic record
Abstract
While there is a wealth of sociolinguistic research on subculture and a rapidly growing field of digital ethnography, little research has been conducted on subcultural language use online. Superfan groups, or stans, form speech communities on Twitter/X and present as a closed group despite remaining public. Through digital ethnographic observation of nonstandard English use on Twitter, I argue that Barbz--Nicki Minaj stans––discourage their posts from spreading to the general public. Working with the algorithm’s composition of social media feeds, Barbz use language to conceal themselves while remaining discoverable. Individuals use language variation and encoding to interact directly with the algorithm, strategically hiding their conversations from the public. By way of sociolinguistic theories including variance and enregisterment, I situate this study in relation to fandom studies, cultural capital, and structural theories of internet. This netnography takes a multimodal approach to social media, showing that Barbz strategically open their community at specific times and in specific ways that are advantageous to them. On Twitter, Barbz employ language to manipulate the borders of both their community and their audience. In order to understand group maintenance, formation, and relationality online it is vital to account for the role of the algorithm as companion rather than structural affordance.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".