在社交媒体上模拟意见两极分化:意大利对Covid-19疫苗接种的犹
Jonathan Franceschi1, Lorenzo Pareschi2, Elena Bellodi3
1Department of Mathematics "F. Casorati", University of Pavia, Pavia, Italy.
PloS one
|October 2, 2023
概括
这项研究模拟了在网上关于疫苗接种的观点的两极分化. 它使用一个结合论动态和假新闻传播的框架来解释观察到的社交媒体数据中的双式论分布.
科学领域:
- 计算社会科学 计算社会科学
- 流行病学 流行病学
- 网络科学 网络科学
背景情况:
- 随着COVID-19的爆发,人们对疫苗接种产生了分歧和错误信息,尤其是在社交媒体上.
- 在线平台经常表现出意见两极分化,观点变得越来越极端和分离.
- 现有的模型可能无法完全捕捉到意见动态和虚假信息传播之间的相互作用.
研究的目的:
- 开发一个数学框架,模拟与假新闻传播相结合的两种不同意见形成动态.
- 分析两种模式的意见分布的出现,表明两极化.
- 将模型应用于有关COVID-19疫苗接种的真实世界社交媒体数据.
主要方法:
- 为两种不同的意见形成动态制定了一个差异框架.
- 这一框架与虚假新闻传播的分隔模型相结合.
- 平均场分析被用来推导出福克-普朗克系统来研究意见分布.
- 该模型使用意大利社交媒体的情绪分析数据进行了验证.
主要成果:
- 福克-普朗克系统成功地复制了双模态的意见分布,与两极化动态一致.
- 数字模拟表明该模型能够描述双模态意见结构的形成.
- 该模型准确地捕获了在社交媒体上的疫苗犹数据集中观察到的两极化效应.
结论:
- 提出的模型有效地解释了公众对疫苗接种意见的两极分化,特别是在在线社区中.
- 数学建模与社交媒体数据分析相结合,为错误信息的传播和意见动态提供了洞察力.
- 了解这些动态对于解决疫苗犹和促进知情的公共话语至关重要.
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