Relations publiques, Big Data et médias sociaux : l’exemple de United Airlines
Bibliographic record
Abstract
Cet article s’intéresse à la crise qui frappa la société aérienne United Airlines (UA) suite à la diffusion d’une vidéo sur les médias sociaux en 2017. À partir de la trace numérique, nous brossons un tableau de la dynamique de cette gestion de crise dans le temps : nombre de messages, analyse des conversations, des interactions, des mots-dièse, des mots clés et des contenus. À cet égard, l’analyse de contenu de la gestion de crise du transporteur aérien UA révèle la réponse inadéquate de l’équipe de relationnistes. En conclusion, nous proposons une réflexion sur les devoirs et obligations de l’organisation à l’heure des médias sociaux, du big data et de l’intelligence artificielle.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".