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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jun 22, 2023
Date Accepted: Sep 18, 2023

The final, peer-reviewed published version of this preprint can be found here:

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis

Unlu A, Truongb S, Tammi T, Lohiniva AL

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis

J Med Internet Res 2023;25:e50199

DOI: 10.2196/50199

PMID: 37862088

PMCID: 10625074

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Exploring Political Mistrust in Pandemic Risk Communication: Insights from Social Media Data Analysis

  • Ali Unlu; 
  • Sophie Truongb; 
  • Tuukka Tammi; 
  • Anna-Leena Lohiniva

ABSTRACT

This research paper investigates the role of trust in health authorities during pandemics, with a specific focus on the temporospatial variance of trust levels and their impact on public health outcomes. The study builds upon a prior study on pandemic-related risk perception in Finland, further exploring twelve identified sub-categories of political mistrust. Using a large-scale dataset of social media interactions over three years (2020-2023), we interrogate the dynamics of political trust, pinpointing areas of mistrust accumulation, fluctuations over time, and changes in topic significance. The dataset consists of 13,629 Twitter and Facebook posts related to COVID-19, obtained using academictwitteR package and Facepager application. We utilize a fine-tuned FinBERT model, achieving an 80% accuracy in predicting political mistrust. Additionally, the BERTopic model is employed for topic modelling, demonstrating superior performance in identifying key themes in the mistrust discourse. Our analysis identifies 43 topics, with the most prominent including COVID-19 mortality, coping strategies, PCR testing, and vaccine efficacy. The study also categorizes these topics into nine major themes, revealing an underlying mistrust in the response of the Finnish Institute for Health and Welfare (THL) to the pandemic. Notably, we uncover that public trust correlates with perceptions of disease severity, information-seeking behavior, and the willingness to adopt health measures. Our findings also underscore the distinctive user profiles across social media platforms and their susceptibility to misinformation. This research highlights the efficacy of computational methods such as natural language processing in managing large-scale user engagement and tracking misinformation during health crises. It also underlines the significance of trust in health authorities for effective risk communication and compliance with health measures. The spread of conspiracy theories emphasizes the need for transparent communication from authorities. In conclusion, our findings reinforce the importance of a holistic approach to public health communication and the central role of trust in managing health crises.


 Citation

Please cite as:

Unlu A, Truongb S, Tammi T, Lohiniva AL

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis

J Med Internet Res 2023;25:e50199

DOI: 10.2196/50199

PMID: 37862088

PMCID: 10625074

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