L’usage des emoji sur Twitter : une grammaire affective entre publics et organisations ?
Bibliographic record
Abstract
Parmi les signes en circulation sur les plateformes du web social, les emoji tiennent un rôle particulier. De nature graphique, ils sont employés autant par les usagers que par les community managers. En analysant ces usages, par une collecte massive de tweets et en les confrontant à des entretiens avec les praticiens, nous partons des emoji pour établir comment s’agence une grammaire des relations entre publics et organisations. Suivre l’emploi des emoji permet alors d’éclairer le travail émotionnel et affectif des community managers ainsi que le type de relation qu’ils peuvent construire, à l’aide de « mots-images » ou de « mots-émotions ». Les emoji agissent alors comme des affordances affectives guidant les pratiques professionnelles dans un environnement mouvant. Il en ressort que l’expression singulière des affections, médiée par des fonctionnalités affectives, permet de nourrir un sentiment général servant l’intérêt des plateformes et accessoirement celui des marques.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".