Official new terms in the age of social media: the story of<i>hashtag</i>on French Twitter
Bibliographic record
Abstract
Abstract As other chapters in this special issue demonstrate, social media proposes widely available data for sociolinguistic analysis. Twitter is an ideal resource to implement variationist approaches regarding regional differences, features specific to gender, and metrics of social media influence. At the same time, official intervention on language use, while somewhat studied in other corpora, is less explored on Twitter. French shows a long tradition of purist and prescriptive ideologies, embodied by the Académie française in France and the Office québécois de la langue française in Québec. The injection of recommended terminology aimed to eradicate foreign influence often has questionable success rate, especially in such an informal setting as Twitter. This article thus investigates lexical variation, in particular, the implantation of official new French translations mot-dièse and mot-clic between 2010 and 2016 that are meant to replace the English word hashtag . Results corroborate previous findings on the lacklustre implantation of the prescribed terms, while also revealing that users in Québec are more inclined to adapt them. Furthermore, diffusion online reflects face-to-face patterns that is cascading spread from large urban areas to smaller cities.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".