دورية أكاديمية
Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis
| العنوان: | Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis |
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| المؤلفون: | Ali Unlu, Sophie Truong, Tuukka Tammi, Anna-Leena Lohiniva |
| المصدر: | Journal of Medical Internet Research, Vol 25, p e50199 (2023) |
| بيانات النشر: | JMIR Publications, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Public aspects of medicine |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270 |
| الوصف: | BackgroundThis 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. ObjectiveThe 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. MethodsData 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. ResultsOur 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. ConclusionsThe 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1438-8871 |
| Relation: | https://www.jmir.org/2023/1/e50199; https://doaj.org/toc/1438-8871 |
| DOI: | 10.2196/50199 |
| URL الوصول: | https://doaj.org/article/4dbaf2e0814d4686b9ee8e3d743680c0 |
| رقم الأكسشن: | edsdoj.4dbaf2e0814d4686b9ee8e3d743680c0 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14388871 |
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| DOI: | 10.2196/50199 |