使用蝴蝶结分解对在线疫苗接种观点进行建模与预测
Modelling and Predicting Online Vaccination Views using Bow-tie Decomposition
AI总结：
本文利用蝴蝶结分解分析反疫苗、支持疫苗和中立 Facebook 页面构成的有向时间网络，发现不同立场群体具有不同的主导网络组件，并通过智能体模拟和机器学习证明引入蝴蝶结结构能改善观点动态预测。
摘要：
自新冠疫情暴发以来，社交媒体在塑造公众疫苗接种观点方面变得越来越重要。本文使用蝴蝶结结构分析一个由有向在线社交网络构成的时间数据集，该数据集表示反疫苗接种、支持疫苗接种和中立 Facebook 页面之间的信息交换。蝴蝶结结构将一个网络分解为七个组成部分，本文重点强调其中两个组成部分“SCC”和“OUT”：SCC 是最大的强连通分量，充当“信息放大器”；OUT 包含所有存在从 SCC 中某个节点出发的有向路径的节点，充当“信息创造者”。我们一致观察到在统计上显著的蝴蝶结结构，并且每个疫苗接种群体在不同时间具有不同的主导组成部分。特别是，反疫苗接种群体具有较大的 OUT，而支持疫苗接种群体具有较大的 SCC。我们进一步使用基于智能体的模拟和机器学习模型，研究以粉丝数量变化衡量的观点随时间的变化。在这两种方法中，考虑蝴蝶结分解都能更好地反映疫苗接种群体之间的信息流差异，并改善我们的观点动态预测结果。我们所考虑的建模框架可应用于任何多立场时间网络，并可为在广泛的应用中使用蝴蝶结结构探索观点动态奠定基础。
英文摘要：
Social media has become increasingly important in shaping public vaccination views, especially since the COVID-19 outbreak. This paper uses bow-tie structure to analyse a temporal dataset of directed online social networks that represent the information exchange among anti-vaccination, pro-vaccination, and neutral Facebook pages. Bow-tie structure decomposes a network into seven components, with two components "SCC" and "OUT" emphasised in this paper: SCC is the largest strongly connected component, acting as an "information magnifier", and OUT contains all nodes with a directed path from a node in SCC, acting as an "information creator". We consistently observe statistically significant bow-tie structures with different dominant components for each vaccination group over time. In particular, the anti-vaccination group has a large OUT, and the pro-vaccination group has a large SCC. We further investigate changes in opinions over time, as measured by fan count variations, using agent-based simulations and machine learning models. Across both methods, accounting for bow-tie decomposition better reflects information flow differences among vaccination groups and improves our opinion dynamics prediction results. The modelling frameworks we consider can be applied to any multi-stance temporal network and could form a basis for exploring opinion dynamics using bow-tie structure in a wide range of applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
