TY  - JOUR
AU  - Unlu, Ali
AU  - Truong, Sophie
AU  - Tammi, Tuukka
AU  - Lohiniva, Anna-Leena
PY  - 2023
DA  - 2023/10/20
TI  - Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis
JO  - J Med Internet Res
SP  - e50199
VL  - 25
KW  - political trust
KW  - social media
KW  - text classification
KW  - topic modeling
KW  - COVID-19
KW  - Finland
KW  - trust
KW  - authority
KW  - public health outcome
KW  - pandemic
KW  - perception
KW  - mistrust
KW  - interaction
KW  - Twitter
KW  - Facebook
KW  - analysis
KW  - computational method
KW  - natural language processing
KW  - misinformation
KW  - communication
KW  - crisis
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e50199
UR  - https://doi.org/10.2196/50199
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37862088
DO  - 10.2196/50199
ID  - info:doi/10.2196/50199
ER  -