L'utilisation performante des médias sociaux en marketing politique : campagne Obama 2012
Bibliographic record
Abstract
Les médias sociaux sont des outils de diffusion instantanée de l'information et des vecteurs de marketing politique importants.Ils permettent non seulement de partager l'information, mais aussi de la garder accessible en quasi-permanence.Cet article recense des modèles d'utilisation stratégique des médias sociaux pour un candidat aux élections américaines, possiblement applicable ailleurs.Les concepts élaborés émanent de la relation entre un discourant et son public.Médias sociaux, prédispositions sociales, stratégies de marketing, propagande politique, encodage et constance sont les concepts clés de ce modèle.La recherche montre d'ailleurs une corrélation intéressante entre l'utilisation des médias sociaux et le choix électoral.Enfin, c'est en liant un bon encodage du discours et la constance de celui-ci par l'utilisation de Facebook, Twitter et Youtube que peut se modéliser l'utilisation performante des médias sociaux en 2012.Reste à voir si ce modèle restera adéquat malgré les avancées technologiques en diffusion d'informations politiques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".