The Impact of Misinformation on Social Media in the Context of Natural Disasters: Narrative Review
Notice bibliographique
Résumé
Background: Misinformation on social media during natural disasters has become a significant challenge, with the potential to increase public confusion, panic, and distrust. Although individuals rely on social media platforms for timely updates during crises, these platforms also facilitate the rapid spread of unverified and misleading information. Consequently, misinformation can hamper emergency response efforts, misdirect resources, and distort public perception of the disaster's true severity. Objective: This narrative review aims to (1) critically evaluate the available evidence; (2) unpack the dynamics of misinformation on social media in the context of natural disasters, specifically natural hazards, shedding light on the challenges, implications, and potential solutions; and (3) develop a conceptual model linking misinformation, public impact, and disasters, grounded in sourced evidence. Methods: The narrative review examines the impact of social media misinformation in the context of natural disasters. The literature search was conducted using the PubMed database and Google Scholar in April 2024. Studies eligible for inclusion were published in English, with no restrictions on publication date, geographic region, or target population. The inclusion criteria focused on the original research that examined social media misinformation related to natural disasters, specifically natural hazards. Results: From an initial pool of 173 studies, 9 studies met the inclusion criteria for this review. The selected studies revealed consistent patterns in how misinformation spreads during natural disasters, highlighting the role of users, some influencers, and bots in amplified false narratives. The misleading messages disseminated across social media platforms often outpaced official communications, resulting in reduced trust and exacerbating anxiety, stress, and fear among affected populations. This heightened emotional response and erosion of trust in official communications influenced an individual's susceptibility to the misinformation and prompted inappropriate actions. Consequently, such actions led to resource misallocation, overwhelmed emergency services, and diverted attention away from genuine needs. Collectively, these factors negatively impacted public health outcomes and diminished the effectiveness of emergency management efforts, as illustrated in the conceptual model developed to provide a greater understanding of this critical area of study. Conclusions: This narrative review highlights the significant impact of misinformation in the context of natural disasters, specifically natural hazards. It stresses the urgent need for disaster preparedness and response plans that include targeted interventions such as real-time misinformation detection technologies, public education campaigns focused on digital literacy, and proactive debunking initiatives. Implementing these strategies can help mitigate the harmful effects of misinformation, strengthen public trust in official communications, enhance the effectiveness of disaster response, and improve public health outcomes.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».