Cargando…
Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis
BACKGROUND: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. OBJECTIVE: The paper aims to investigate variations in trust levels...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625074/ https://www.ncbi.nlm.nih.gov/pubmed/37862088 http://dx.doi.org/10.2196/50199 |
_version_ | 1785131050888855552 |
---|---|
author | Unlu, Ali Truong, Sophie Tammi, Tuukka Lohiniva, Anna-Leena |
author_facet | Unlu, Ali Truong, Sophie Tammi, Tuukka Lohiniva, Anna-Leena |
author_sort | Unlu, Ali |
collection | PubMed |
description | BACKGROUND: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. OBJECTIVE: The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. METHODS: Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. RESULTS: Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. CONCLUSIONS: The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively. |
format | Online Article Text |
id | pubmed-10625074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-106250742023-11-05 Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis Unlu, Ali Truong, Sophie Tammi, Tuukka Lohiniva, Anna-Leena J Med Internet Res Original Paper BACKGROUND: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. OBJECTIVE: The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. METHODS: Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. RESULTS: Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. CONCLUSIONS: The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively. JMIR Publications 2023-10-20 /pmc/articles/PMC10625074/ /pubmed/37862088 http://dx.doi.org/10.2196/50199 Text en ©Ali Unlu, Sophie Truong, Tuukka Tammi, Anna-Leena Lohiniva. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Unlu, Ali Truong, Sophie Tammi, Tuukka Lohiniva, Anna-Leena Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title_full | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title_fullStr | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title_full_unstemmed | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title_short | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
title_sort | exploring political mistrust in pandemic risk communication: mixed-method study using social media data analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625074/ https://www.ncbi.nlm.nih.gov/pubmed/37862088 http://dx.doi.org/10.2196/50199 |
work_keys_str_mv | AT unluali exploringpoliticalmistrustinpandemicriskcommunicationmixedmethodstudyusingsocialmediadataanalysis AT truongsophie exploringpoliticalmistrustinpandemicriskcommunicationmixedmethodstudyusingsocialmediadataanalysis AT tammituukka exploringpoliticalmistrustinpandemicriskcommunicationmixedmethodstudyusingsocialmediadataanalysis AT lohinivaannaleena exploringpoliticalmistrustinpandemicriskcommunicationmixedmethodstudyusingsocialmediadataanalysis |