模拟和预测在线接种疫苗的观点,使用蝶结分解

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Yueting Han^1,2, Marya Bazzi^2,3, Paolo Turrini⁴

¹MathSys CDT, University of Warwick, Coventry, UK.

Royal Society open science

|February 22, 2024

概括

社交媒体极大地影响了人们对疫苗的看法. 使用蝶结结构分析Facebook网络,发现了支持疫苗接种 (大型SCC) 和反对疫苗接种 (大型OUT) 群体的不同信息流模式,改善了意见预测.

科学领域:

网络科学网络科学
计算社会科学计算社会科学
公共卫生传播公共卫生传播

背景情况:

社交媒体平台对于塑造关于疫苗接种的公共话语至关重要.
随着COVID-19大流行,社交媒体在疫苗接种方面的作用得到了扩大.
了解在线社交网络中的信息流对于公共卫生至关重要.

研究的目的:

分析与疫苗接种相关的Facebook网络中的信息交换动态.
将蝶结结构分析应用于时间社交网络数据.
调查网络结构如何影响论动态和预测准确度.

主要方法:

利用来自Facebook页面的定向在线社交网络的时间数据集.
应用蝶结结构分解来分析网络组件 (SCC和OUT).
采用基于代理的模拟和机器学习模型来研究意见动态和粉丝数量变化.

主要成果:

随着时间的推移,在疫苗接种组中始终观察到统计学上显著的蝶结结构.
确定了不同的主导组件:反疫苗组显示出一个大的OUT组件 ("信息创建者"),而支持疫苗的组具有一个大的SCC ("信息放大器").
考虑到蝶结的分解,提高了论动态预测模型的准确性.

结论:

蝶结结构有效地区分了社交媒体上的疫苗接种话语中的信息流模式.
支持和反对疫苗接种群体的独特网络结构影响了信息传播.
拟议的建模框架提供了一种多功能方法,用于分析各种应用中的多站式时间网络中的意见动态.

关键词:

计算社会科学计算社会科学数据分析数据分析数据分析意见的动态意见的动态社交网络社交网络社会心理学社会心理学

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