Public Officials’ Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets
Notice bibliographique
Résumé
BACKGROUND: Social media is an important way for governments to communicate with the public. This is particularly true in times of crisis, such as the COVID-19 pandemic, during which government officials played a strong role in promoting public health measures such as vaccines. OBJECTIVE: In Canada, provincial COVID-19 vaccine rollout was delivered in 3 phases aligned with federal government COVID-19 vaccine guidance for priority populations. In this study, we examined how Canadian public officials used Twitter to engage with the public about vaccine rollout and how this engagement has shaped public response to vaccines across jurisdictions. METHODS: We conducted a content analysis of tweets posted between December 28, 2020, and August 31, 2021. Leveraging the social media artificial intelligence tool Brandwatch Analytics, we constructed a list of public officials in 3 jurisdictions (Ontario, Alberta, and British Columbia) organized across 6 public official types and then conducted an English and French keyword search for tweets about vaccine rollout and delivery that mentioned, retweeted, or replied to the public officials. We identified the top 30 tweets with the highest impressions in each jurisdiction in each of the 3 phases (approximately a 26-day window) of the vaccine rollout. The metrics of engagement (impressions, retweets, likes, and replies) from the top 30 tweets per phase in each jurisdiction were extracted for additional annotation. We specifically annotated sentiment toward public officials' vaccine responses (ie, positive, negative, and neutral) in each tweet and annotated the type of social media engagement. A thematic analysis of tweets was then conducted to add nuance to extracted data characterizing sentiment and interaction type. RESULTS: Among the 6 categories of public officials, 142 prominent accounts were included from Ontario, Alberta, and British Columbia. In total, 270 tweets were included in the content analysis and 212 tweets were direct tweets by public officials. Public officials mostly used Twitter for information provision (139/212, 65.6%), followed by horizontal engagement (37/212, 17.5%), citizen engagement (24/212, 11.3%), and public service announcements (12/212, 5.7%). Information provision by government bodies (eg, provincial government and public health authorities) or municipal leaders is more prominent than tweets by other public official groups. Neutral sentiment accounted for 51.5% (139/270) of all the tweets, whereas positive sentiment was the second most common sentiment (117/270, 43.3%). In Ontario, 60% (54/90) of the tweets were positive. Negative sentiment (eg, public officials criticizing vaccine rollout) accounted for 12% (11/90) of all the tweets. CONCLUSIONS: As governments continue to promote the uptake of the COVID-19 booster doses, findings from this study are useful in informing how governments can best use social media to engage with the public to achieve democratic goals.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».