基于集群的BERTopic建模在瑞典的COVID-19疫苗帖子上
Dimitrios Kokkinakis1, Mia-Marie Hammarlin2
1University of Gothenburg, Sweden.
这项研究分析了瑞典论坛Flashback上的COVID-19疫苗讨论,确定了关键主题和用户观点. 该研究提供了关于瑞典围绕疫苗接种的公共话语的见解.
科学领域:
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 像Flashback这样的在线论坛是公共话语的重要平台.
- 围绕COVID-19疫苗和疫苗接种的讨论在全球和瑞典普遍存在.
- 了解公众情绪和普遍的主题对于公共卫生传播至关重要.
研究的目的:
- 探索在瑞典论坛Flashback上的COVID-19疫苗和疫苗接种讨论中普遍存在的主题.
- 在这些讨论中区分积极和消极的观点.
- 提供细微的见解,了解关于疫苗接种的公共话语的多面性质.
主要方法:
- 使用BERTopic,这是一个主题建模框架,利用预先训练的语言模型.
- 应用聚类技术来识别14个相关讨论线程中的主流话题.
- 分析了帖子,以区分关于疫苗的积极和消极情绪.
主要成果:
- 在COVID-19疫苗讨论中确定了总体主题和主导主题.
- 量化和描述了用户表达的积极和消极观点.
- 揭示了论坛内关于疫苗接种的观点的复杂性和多样性.
结论:
- 这项研究为瑞典关于COVID-19疫苗的在线讨论的性质提供了有价值的见解.
- 调查结果强调了监测公共论坛的重要性,以了解疫苗的情绪.
- 结果可以为针对性公共卫生沟通策略提供有关疫苗接种的信息.
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