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Record W3204891610 · doi:10.3917/comla1.208.0269

L’usage des emoji sur Twitter : une grammaire affective entre publics et organisations ?

2021· article· fr· W3204891610 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunication & langages · 2021
Typearticle
Languagefr
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Montréal
Fundersnot available
KeywordsEmojiHumanitiesPublicsSociologyPolitical sciencePhilosophySocial media

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.066
GPT teacher head0.302
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it