Crisis and Emergency Risk Communication (CERC) in Social Media: A Bibliometric Analysis (Preprint)
Notice bibliographique
Résumé
Background: Crisis and emergency risk communication (CERC) has been proposed as a way for government and public health organizations to improve their ability to connect with the public via social media while also integrating risk communication and crisis communication.However, few studies have systematically analyzed how CERC can be integrated with social media.Objective: Exploring the application of CERC in social media, comparing the distribution of platforms, the types and forms of data, the primary methods used, and clearer understanding of the main contributions and developmental context of CERC.Methods: We used the Web of Science, Scopus, and PubMed as databases to conduct a retrospective review with "Crisis and Emergency Risk Communication" as the search term from 2005 to 2021.Then, CERC was quantitatively and qualitatively analyzed.In the first part, we consider quantitative analysis as the core and publications as the research object, comprehensively covering the publication distribution in terms of time, periodical, country, institution, author, and discipline.In the second part, we use qualitative analysis to explore the composition of research topics related to keywords and the temporal variation trend of research topics based on keywords.The third part focuses on exploring the combination of CERC and social media.Finally, we propose future research routes based on the above results.Results: Health Promotion Practice is the most productive journal with seven contributing publications.The USA has become the dominant player in this field, with a considerable advantage of 28 publications.Australia, China, and the UK are far behind USA's contribution, although they are the second highest contributors.Centers for Disease Control and Prevention is the most contributing organization with three publications.Only seven people published more than two articles at the author level, among whom Lu, J. is from China, while Reynolds, B. and the other five are all from the United States.The cluster results show that the researchers mainly focus on five categories of issues: prevention and control of infectious diseases, disaster planning of bioterrorism, social media's role in risk and crisis communication, medical intervention in risk perception, the role of Twitter in public health crises. Conclusions:We raise some future concerns based on the use case of CERC in social media.First, existing research has focused too much on Twitter and may have overlooked the role of Facebook.Second, existing studies have focused on only one side of the government (organization) or the public, and have not considered the effectiveness of government or organizational communication strategies as assessed by public response.Third, the methodologies for studying tweets must be updated.Fourth, the research on false information in the precrisis stage must improve.Finally, the information forms of crisis communication of governments or organizations must be enriched.
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 | 0,036 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,040 | 0,081 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».