The Use of Facebook Advertising for Communicating Public Health Messages: A Campaign Against Drinking During Pregnancy in New Zealand
Notice bibliographique
Résumé
BACKGROUND: Social media is gaining recognition as a platform for delivering public health messages. One area attracting attention from public health researchers and professionals is Facebook's advertising channel. This channel is reported to have a broad reach and generate high user engagement with the disseminated campaign materials. However, to date, no study has examined the communication process via this channel which this study aimed to address. OBJECTIVE: The specific objectives of the study were to (1) examine user engagement for a public health campaign based on the metadata provided by Facebook, (2) analyze comments generated by the campaign materials using text mining, and (3) investigate the relationship between the themes identified in the comments and the message and the sentiments prevalent in the themes that exhibited significant relationships. METHODS: This study examined a New Zealand public health pilot campaign called "Don't Know? Don't Drink," which warned against drinking alcohol during pregnancy. The campaign conveyed the warning through a video and three banner ads that were delivered as news feeds to women aged 18-30 years. Thematic analysis using text mining performed on the comments (n=819) identified four themes. Logistic regression was used to identify meaning-making themes that exhibited association with the message. RESULTS: The users' engagement was impressive with the video receiving 203,754 views. The combined likes and shares for the promotional materials (video and banner ads) amounted to 6125 and 300, respectively. The logistic regression analysis showed two meaning-making themes, namely, risk of pregnancy (P=.003) and alcohol and culture (P<.001) exhibited association with the message. The sentiment analysis carried out on the two themes revealed there were more negative than positive comments (47% vs 28%). CONCLUSIONS: The user engagement observed in this study was consistent with previous research. The numbers reported for views, likes, and shares may be seen as unique interactions over the fixed period of the campaign; however, survey research would be required to find out the true evaluative worth of these metadata. A close examination of the comments, employing text mining, revealed that the message was not accepted by a majority of the target segment. Self-identity and conformity theories may help to explain these observed reactions, albeit warrant further investigations. Although the comments were predominantly negative, they provide opportunities to engage back with the women. The one-way communication format followed in this campaign did not support any two-way engagement. Further investigation is warranted to establish whether using a two-way communication format would have improved the acceptability of such public health messages delivered via social media. The findings of this study caution using a one-way communication format to convey public health messages via Facebook's advertising channel.
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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».