通过在线社交网络中使用高级中心性指标分析健康错误信息
Mkululi Sikosana1, Sean Maudsley-Barton1, Oluwaseun Ajao1
1Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
PLOS digital health
|June 16, 2025
概括
新的中心性指标有效地识别了在线社交网络 (OSN) 上更多的健康错误信息传播者. 这些先进的方法改善了干预策略,大大减少了危机期间健康错误信息的影响.
科学领域:
- 网络科学 网络科学
- 公共卫生信息学 公共卫生信息学
- 计算社会科学 计算社会科学
背景情况:
- 在线社交网络 (OSN) 在全球危机期间促进了健康错误信息的快速传播.
- 传统的中心性指标与现代在线网络的复杂性和活力作斗争.
- 有效识别错误信息影响者对于公共卫生干预至关重要.
研究的目的:
- 介绍和比较新的中心性指标 (动态影响中心性,健康错误信息脆弱性中心性,传播中心性) 与传统方法.
- 评估这些新指标在识别健康错误信息的有影响力的节点和传播途径方面的有效性.
- 评估新型指标的干预措施对减少健康错误信息传播的影响.
主要方法:
- 开发并应用了三个新的中心性指标:动态影响中心性 (DIC),健康错误信息脆弱性中心性 (MVC) 和传播中心性 (PC).
- 使用FibVID数据集对传统和新型指标进行比较,以确定有影响力的节点和途径.
- 在第二个数据集,Monant医疗错误信息上验证了框架,以评估概括性.
- 根据已识别的有影响力的节点评估干预的有效性.
主要成果:
- 与单独使用传统指标相比,新型指标发现了44.83%更多独特的有影响力的节点.
- 基于新型指标的干预措施实现了62.5%的健康错误信息减少,比基线干预措施提高了25%.
- 先进的指标证明了在不同的健康错误信息数据集中成功的概括.
结论:
- 传统和新型中心性措施的结合为了解健康错误信息动态提供了更全面的框架.
- 拟议的指标有助于识别在线健康错误信息网络中的关键参与者和途径.
- 这种方法提供了一个强有力的和可通用的策略,以减轻在各种在线环境中传播健康错误信息的传播.
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