Sikosana Mkululi, Maudsley-Barton Sean, Ajao Oluwaseun
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
PLOS Digit Health. 2025 Jun 16;4(6):e0000888. doi: 10.1371/journal.pdig.0000888. eCollection 2025 Jun.
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.
在诸如新冠疫情这样的全球危机期间，健康错误信息在在线社交网络（OSN）上的迅速传播对公众健康、社会稳定和机构信任构成了挑战。长期以来，中心性指标在理解信息流动态方面一直起着关键作用，尤其是在健康错误信息的背景下。然而，在线网络日益增加的复杂性和动态性，特别是在危机期间，凸显了这些传统方法的局限性。本研究引入并比较了三种新颖的中心性指标：动态影响中心性（DIC）、健康错误信息易感性中心性（MVC）和传播中心性（PC）。这些指标纳入了时间动态、易感性和多层网络交互。使用FibVID数据集，我们比较了传统指标和新颖指标，以识别有影响力的节点、传播途径和错误信息影响者。传统指标识别出29个有影响力的节点，而新指标发现了24个独特节点，从而产生了42个组合节点，增加了44.83%。基线干预措施将健康错误信息减少了50%，而纳入新指标后这一比例提高到了62.5%，提高了25%。为了评估所提出指标的更广泛适用性，我们在第二个数据集Monant医疗错误信息上验证了我们的框架，该数据集涵盖了新冠疫情之外的各种健康错误信息讨论。结果证实，先进指标成功地进行了推广，识别出了传统方法未捕捉到的不同有影响力的行为者。总体而言，研究结果表明，传统和新颖的中心性测量方法相结合，为理解和减轻不同在线网络环境中健康错误信息的传播提供了一个更强大、更具通用性的框架。
