Mpox on Instagram: A content analytic study (Preprint)
Notice bibliographique
Résumé
Abstract Background Mpox was declared a public health emergency of international concern in 2022. Instagram is widely used by age groups and communities disproportionately affected by mpox; yet platform-specific evidence on mpox information characteristics and engagement remains limited. Objective The aim of this study is to characterize sources, content, and engagement features of mpox-related Instagram posts, to describe prevention and treatment framing, and to compare the top 10% most-liked posts with the remaining corpus. Methods We retrieved English-language public Instagram posts via CrowdTangle containing “mpox” or “monkeypox” dated May 5, 2022, to January 17, 2023 (initial N=18,616). Using a pretested, deductive codebook adapted from prior Instagram health studies, 2 coders completed 2 pilot rounds; variables with low agreement were excluded. A randomized analytic sample of 1000 posts was coded for source type, content features, and prevention/treatment framing. Descriptive statistics were computed. For engagement contrasts, we compared the top 10% most-liked posts with the bottom 90% using tests of differences in independent proportions (mean differences [MD] with P values). Results Most posts originated from organizations (760/1000, 76%) versus individuals (240/1000, 24%). Organizational sources most commonly included businesses (436/760, 57.4%) and news/media outlets (401/760, 52.8%); government (174/760, 22.9%), nonprofits (131/760, 17.3%), and health care organizations (70/760, 9.2%) were less frequent. About one-third of posts cited a source (344/1000, 34.4%), most often the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC)/other federal entity. Posts predominantly used illustrated images/infographics (827/1000, 82.7%); photos appeared in 47.3% (473/1000) and videos in 12.4% (124/1000) of posts. Prevention content appeared in 38.4% (384/1000) of posts, most commonly vaccination (684/1000, 68.5% of prevention posts), followed by avoiding close contact (145/1000, 14.5%), avoiding contact with objects (83/1000, 8.3%), abstaining from sexual activity (76/1000, 7.6%), and condom use (13/1000, 1.3%); 28.9% (289/1000) of prevention posts noted barriers. Treatment mentions were uncommon (25/1000, 2.5% traditional biomedical; 2/1000, 0.2% alternative). Compared with the bottom 90%, the top 10% most-liked posts (1) were more likely to originate from public figures/celebrities among individuals (MD=−0.591; P <.001) and from businesses (MD=−0.299; P <.001) or news/media (MD=−0.350; P <.001) among organizations; (2) were less likely to be from government ( P <.001) , nonprofit ( P =.006), or health care organizations ( P =.005); and (3) more often included nonmoving images (MD=−0.119; P =.024), visible lesion depictions (MD=−0.081; P =.035), prevalence mentions (MD=−0.180; P <.001), and citations (MD=−0.162; P =.001). Conclusions During the initial outbreak period, the highly engaged mpox content on Instagram skewed toward posts by public figures and news/business accounts and toward static, citation-bearing visuals that included prevalence context and occasionally lesion imagery. Public-health communicators seeking reach on Instagram should prioritize clear static infographics with explicit source citation and epidemiologic context and consider copublishing with trusted creators and news outlets, while addressing access barriers highlighted in prevention posts.
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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 | 0,005 | 0,028 |
| 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 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».