Crisis Communication About the Maui Wildfires on TikTok: Content Analysis of Engagement With Maui Wildfire–Related Posts Over 1 Year
Notice bibliographique
Résumé
Background: The August 2023 wildfire in the town of Lāhainā on the island of Maui in Hawai'i caused catastrophic damage, affecting thousands of residents, and killing 102 people. Social media platforms, particularly TikTok, have become essential tools for crisis communication during disasters, providing real-time crisis updates, mobilizing relief efforts, and addressing misinformation. Understanding how disaster-related content is disseminated and engaged with on these platforms can inform strategies for improving emergency communication and community resilience. Objective: Guided by Social-Mediated Crisis Communication theory, this study examined TikTok posts related to the Maui wildfires to assess content themes, public engagement, and the effectiveness of social media in disseminating disaster-related information. Methods: TikTok posts related to the Maui wildfires were collected from August 8, 2023, to August 9, 2024. Using TikTok's search functionality, we identified and reviewed public posts that contained relevant hashtags. Posts were categorized into 3 periods: during the disaster (August 8 to August 31, 2023), the immediate aftermath (September 1 to December 31, 2023), and the long-term recovery (January 1 to August 9, 2024). Two researchers independently coded the posts into thematic categories, achieving an interrater reliability of 87%. Engagement metrics (likes and shares) were analyzed to assess public interaction with different themes. Multivariable linear regression models were used to examine the associations between log-transformed likes and shares and independent variables, including time intervals, video length, the inclusion of music or effects, content themes, and hashtags. Results: A total of 275 TikTok posts were included in the analysis. Most posts (132/275, 48%) occurred in the immediate aftermath, while 76 (27.6%) were posted during the long-term recovery phase, and 24.4% (n=67) were posted during the event. Posts during the event garnered the highest average number of likes (mean 75,092, SD 252,759) and shares (mean 10,928, SD 55,308). Posts focused on "Impact & Damage" accounted for the highest engagement, representing 36.8% (4,090,574/11,104,031) of total likes and 61.2% (724,848/1,184,049) of total shares. "Tourism Impact" (2,172,991/11,104,031, 19.6% of likes; 81,372/1,184,049, 6.9% of shares) and "Relief Efforts" (509,855/11,104,031, 4.6% of likes; 52,587/1,184,049, 4.4% of shares) were also prominent themes. Regression analyses revealed that videos with "Misinformation & Fake News" themes had the highest engagement per post, with a 4.55 coefficient for log-shares (95% CI 2.44-6.65), while videos about "Tourism Impact" and "Relief Efforts" also showed strong engagement (coefficients for log-likes: 2.55 and 1.76, respectively). Conclusions: TikTok is an influential tool for disaster communication, amplifying both critical disaster updates and misinformation, highlighting the need for strategic content moderation and evidence-based messaging to enhance the platform's role in crisis response. Public health officials, emergency responders, and policy makers can leverage TikTok's engagement patterns to optimize communication strategies, improve real-time risk messaging, and support long-term community resilience.
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 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».