PENGARUH KAMPANYE ‘DOGS ARE NOT FOOD’ YANG DILAKUKAN DI AKUN TWITTER @ALBERTHIENEE TERHADAP PEMAHAMAN MASYARAKAT
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Notice bibliographique
Résumé
Social media users today are not only use their account for communicate and interact with other people, but also use it for a campaign tool. Alberthiene Endah uses her Twitter account to attempting ‘Dogs Are Not Food' campaign, also to increasing people's awareness and cognitive concerned dogs are definitely not human's food. This study aims to determine how ‘Dogs Are Not Food' campaign could influence people's cognitive. The data analytical uses quantitative approach and multiple regression test with two independent variables, namely Volume (X1) and Engagement (X2), also dependent variable namely cognitive (Y). The samples gaining 263 respondents whom followed @AlberthieneE's Twitter account.The F-Test derived obvious influence between volume and engagement towards people's cognitive simultanously. Then the T-Test derived obvious influence either volume or engagement towards people's cognitive partially. According to determination coefficient, Adjusted R Square result points out 0,482, which means the variation of dependent variable can be explained by independent variables as much as 48,2%, the rest (51,8%) is influenced by another factors. According to regression coefficient result, X1 points out 1,047 meanwhile X2 points out 0,488. It examines that coefficient X1 is higher than X2, which means people's cognitive concerning 'Dogs Are Not Food' campaign strongly more influenced by volume than engagement. The overall result, it stated the better assessment from people about volume and engagement @AlberthieneE Twitter account, the better people's cognitive concerning 'Dogs Are Not Food' campaign also. Keyword: social media, Twitter, campaign, people's cognitive, Alberthiene Endah, ‘Dogs Are Not Food' DAFTAR PUSTAKA Adler, E. S., & Clark, R. (2011). An Invitation to Social Research: How It's Done (4th ed.). Belmont: Wadsworth-Cengage Learning. Akun Twitter @AlberthieneE. 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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle