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The Prevalence of mRNA Related Discussions During the Post-COVID-19 Era.
Kokkinakis, Dimitrios; Bruinsma, Bastiaan; Hammarlin, Mia-Marie.
  • Kokkinakis D; University of Gothenburg and the Centre for Ageing & Health, AgeCap, Sweden.
  • Bruinsma B; Chalmers University of Technology, Sweden.
  • Hammarlin MM; University of Lund, Department of Communication and Media, Sweden.
Stud Health Technol Inform ; 302: 798-802, 2023 May 18.
Article in English | MEDLINE | ID: covidwho-2324162
ABSTRACT
Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy and skepticism are raising serious concerns for a portion of the population in many countries, including Sweden. In this study, we use Swedish social media data and structural topic modeling to automatically identify mRNA-vaccine related discussion themes and gain deeper insights into how people's refusal or acceptance of the mRNA technology affects vaccine uptake. Our point of departure is a scientific study published in February 2022, which seems to once again sparked further suspicion and concern and highlight the necessity to focus on issues about the nature and trustworthiness in vaccine safety. Structural topic modelling is a statistical method that facilitates the study of topic prevalence, temporal topic evolution, and topic correlation automatically. Using such a method, our research goal is to identify the current understanding of the mechanisms on how the public perceives the mRNA vaccine in the light of new experimental findings.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2023 Document Type: Article Affiliation country: Shti230269

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2023 Document Type: Article Affiliation country: Shti230269