标题： 使用在线社交网络中的高级中心性度量分析健康错误信息 显示英文标题

标题： Analysing Health Misinformation with Advanced Centrality Metrics in Online Social Networks

摘要： 在线社交网络（OSN）在诸如新冠疫情等全球危机期间健康错误信息的迅速传播对公共卫生、社会稳定和制度信任构成了挑战。中心性度量长期以来在理解信息流动的动力学方面起着关键作用，尤其是在健康错误信息的背景下。然而，网络的日益复杂性和动态性，特别是在危机期间，突显了这些传统方法的局限性。本研究引入并比较了三种新的中心性度量：动态影响力中心性（DIC）、健康错误信息易感中心性（MVC）和传播中心性（PC）。这些度量结合了时间动态性、易感性和多层网络交互。使用FibVID数据集，我们比较了传统和新度量以识别有影响力的节点、传播路径和错误信息传播者。传统度量识别出29个有影响力节点，而新度量发现了24个独特节点，总共得到42个节点，增加了44.83%。基线干预措施减少了50%的健康错误信息，而结合新度量后将其提高到62.5%，提高了25%。为了评估所提出度量的更广泛应用性，我们在第二个数据集Monant医学错误信息上验证了我们的框架，该数据集涵盖了除新冠疫情以外的各种健康错误信息讨论。结果证实，先进的度量能够成功推广，识别出了传统方法未能捕捉到的不同有影响力人物。总体而言，研究结果表明，传统和新中心性度量的结合为在不同在线网络环境中理解和减轻健康错误信息传播提供了一个更强大且可推广的框架。 显示英文摘要

摘要： The rapid spread of health misinformation on online social networks (OSNs) during global crises such as the COVID-19 pandemic poses challenges to public health, social stability, and institutional trust. Centrality metrics have long been pivotal in understanding the dynamics of information flow, particularly in the context of health misinformation. However, the increasing complexity and dynamism of online networks, especially during crises, highlight the limitations of these traditional approaches. This study introduces and compares three novel centrality metrics: dynamic influence centrality (DIC), health misinformation vulnerability centrality (MVC), and propagation centrality (PC). These metrics incorporate temporal dynamics, susceptibility, and multilayered network interactions. Using the FibVID dataset, we compared traditional and novel metrics to identify influential nodes, propagation pathways, and misinformation influencers. Traditional metrics identified 29 influential nodes, while the new metrics uncovered 24 unique nodes, resulting in 42 combined nodes, an increase of 44.83%. Baseline interventions reduced health misinformation by 50%, while incorporating the new metrics increased this to 62.5%, an improvement of 25%. To evaluate the broader applicability of the proposed metrics, we validated our framework on a second dataset, Monant Medical Misinformation, which covers a diverse range of health misinformation discussions beyond COVID-19. The results confirmed that the advanced metrics generalised successfully, identifying distinct influential actors not captured by traditional methods. In general, the findings suggest that a combination of traditional and novel centrality measures offers a more robust and generalisable framework for understanding and mitigating the spread of health misinformation in different online network contexts.