Finnish Institute for Health and Welfare, Helsinki, Finland.
Department of Computer Science, Aalto University, Espoo, Finland.
J Med Internet Res. 2023 Oct 20;25:e50199. doi: 10.2196/50199.
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.
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.
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.
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.
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.
本研究扩展了芬兰健康与福利研究所先前关于大流行相关风险认知的研究,重点关注对卫生当局的信任及其对公共卫生结果的影响。
本文旨在调查随时间推移和跨社交媒体平台的信任水平变化,以及进一步探讨 12 个政治不信任亚类。它旨在了解政治信任的动态,包括不信任的积累、随时间的波动以及主题相关性的变化。此外,该研究旨在将定性研究结果与通过计算方法获得的结果进行比较。
数据来自一个大规模数据集,其中包含 2020 年至 2023 年间与 COVID-19 相关的 13629 条来自 Twitter 和 Facebook 的帖子。为了进行分析,使用了一个 Fine-tuned FinBERT 模型,其准确率为 80%,用于预测政治不信任。还使用了 BERTopic 模型进行卓越的主题建模性能。
我们的初步分析确定了 43 个与不信任相关的主题,分为 9 个主要主题。最突出的主题包括 COVID-19 死亡率、应对策略、聚合酶链反应测试和疫苗功效。与对权威的不信任相关的论述与对疾病严重程度、采取健康措施的意愿和信息搜索行为的看法有关。我们的研究结果表明,Facebook 和 Twitter 的不同用户参与机制和平台特征促成了大流行期间不同的不信任模式和对错误信息的易感性。
该研究强调了自然语言处理等计算方法在管理大规模参与和错误信息方面的有效性。它强调了对卫生当局的信任对有效风险沟通和公众遵守的重要性。研究结果还强调了当局进行透明沟通的必要性,并得出结论,综合的公共卫生沟通方法对于有效管理卫生危机至关重要。