The Prevalence of mRNA Related Discussions during the Post-COVID-19 Era
(2023) 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 In Studies in Health Technology and Informatics 302. p.798-802- 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... (More)
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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- author
- Kokkinakis, Dimitrios ; Bruinsma, Bastiaan and Hammarlin, Mia Marie LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- event detection, mRNA vaccines, natural language processing, structural topic modeling, Swedish internet forum, Swedish tweets, vaccine hesitancy
- host publication
- Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023
- series title
- Studies in Health Technology and Informatics
- editor
- Hagglund, Maria ; Blusi, Madeleine ; Bonacina, Stefano ; Nilsson, Lina ; Madsen, Inge Cort ; Pelayo, Sylvia ; Moen, Anne ; Benis, Arriel ; Lindskold, Lars and Gallos, Parisis
- volume
- 302
- pages
- 5 pages
- publisher
- IOS Press
- conference name
- 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
- conference location
- Gothenburg, Sweden
- conference dates
- 2023-05-22 - 2023-05-25
- external identifiers
-
- pmid:37203498
- scopus:85159764697
- ISSN
- 1879-8365
- 0926-9630
- ISBN
- 9781643683881
- DOI
- 10.3233/SHTI230269
- language
- English
- LU publication?
- yes
- id
- 170ddbb1-5a63-44a1-8a6b-2346ecf18d40
- date added to LUP
- 2023-08-21 12:41:18
- date last changed
- 2024-04-20 01:06:09
@inproceedings{170ddbb1-5a63-44a1-8a6b-2346ecf18d40, abstract = {{<p>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.</p>}}, author = {{Kokkinakis, Dimitrios and Bruinsma, Bastiaan and Hammarlin, Mia Marie}}, booktitle = {{Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023}}, editor = {{Hagglund, Maria and Blusi, Madeleine and Bonacina, Stefano and Nilsson, Lina and Madsen, Inge Cort and Pelayo, Sylvia and Moen, Anne and Benis, Arriel and Lindskold, Lars and Gallos, Parisis}}, isbn = {{9781643683881}}, issn = {{1879-8365}}, keywords = {{event detection; mRNA vaccines; natural language processing; structural topic modeling; Swedish internet forum; Swedish tweets; vaccine hesitancy}}, language = {{eng}}, pages = {{798--802}}, publisher = {{IOS Press}}, series = {{Studies in Health Technology and Informatics}}, title = {{The Prevalence of mRNA Related Discussions during the Post-COVID-19 Era}}, url = {{http://dx.doi.org/10.3233/SHTI230269}}, doi = {{10.3233/SHTI230269}}, volume = {{302}}, year = {{2023}}, }