Public Health Communication and Engagement on Social Media during the COVID-19 Pandemic (Preprint)
Notice bibliographique
Résumé
BACKGROUND Social media provides governments the opportunity to directly communicate with their constituents. During a pandemic, reaching as many citizens as possible with health messaging is critical to reducing the spread of the disease. This study evaluates efforts to spread healthcare information by Canadian local, provincial, and federal governments during the first five months of the COVID-19 pandemic. OBJECTIVE This study explores engagement patterns with government COVID-19 information shared on social media. It quantitatively evaluates platform-specific dynamics, including meta-data of posts such as account type, number of followers, type of content included, and time of post. It then performs exploratory, theory-building content analysis on outlier communications to identify previously under-examined features that contribute to engagement. METHODS We collect all health-related communications coming from government accounts on Facebook and Twitter and analyze the data using a nested mixed method approach. We first identify quantifiable features linked with citizen engagement. Then, we perform content analysis on those posts with the highest and highest negative residuals to identify content-specific engagement patterns. RESULTS We find considerable within and cross-platform heterogeneity in the relationship between embedded media type and engagement with public health information on social media. On Twitter, public health tweets containing videos receive 121 percent more engagements than those which are text-only, at P<.001. Images receive 35 percent more at P<.001. On Facebook text statuses dominate, with links receiving 39 percent fewer engagements, at P<.001, and videos a 26 percent decrease, also at P<.001. Even more, we find that who posts is more important than what is posted. Controlling for different audience sizes, tweets from the Prime Minister generate 727 percent more engagements than city governments', at P<.001. On Facebook this increases to 5640 percent, still at P<.001. The discrepancy between local and national accounts is larger on Facebook, where mayors, city governments, and local health authorities receive the least engagement. On both platforms premiers and provincial health authorities receive the second and third highest levels of engagement, highlighting the importance of sub-national officials in public health communication. All of these estimates are statistically significant at P<.001. In our qualitative analysis, we find a consistent relationship between content and over- or under performance, relative to our predicted levels of engagement. Concise messages with direct appeals are overrepresented among posts with large positive residuals, as are those which include high quality media, or which leverage pop-culture references or influencers. On the other hand, low quality video and infographics, lengthy policy descriptions, and negative news routinely generate fewer engagements than predicted. CONCLUSIONS We make two critical contributions to existing knowledge about government communication, particularly during public health crises. We identify and theorize cross-platform variations in strategy effectiveness and draw attention to specific, evidence-based practices that can increase engagement with government health information.
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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 | 0,003 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».