Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao
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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.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000888"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169528/pdf/","citationCount":"0","resultStr":"{\"title\":\"Analysing health misinformation with advanced centrality metrics in online social networks.\",\"authors\":\"Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao\",\"doi\":\"10.1371/journal.pdig.0000888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. 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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. 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引用次数: 0
摘要
在COVID-19大流行等全球危机期间,在线社交网络(OSNs)上健康错误信息的迅速传播对公共卫生、社会稳定和机构信任构成了挑战。长期以来,中心性指标一直是理解信息流动态的关键,特别是在卫生错误信息的背景下。然而,在线网络日益增加的复杂性和活力,特别是在危机期间,突出了这些传统方法的局限性。本研究介绍并比较了三种新的中心性指标:动态影响中心性(DIC)、健康错误信息漏洞中心性(MVC)和传播中心性(PC)。这些指标包括时间动态、易感性和多层网络相互作用。使用fivid数据集,我们比较了传统和新的指标,以确定有影响的节点、传播途径和错误信息的影响者。传统指标确定了29个有影响力的节点,而新指标发现了24个独特的节点,总共有42个节点,增加了44.83%。基线干预措施将健康错误信息减少了50%,而采用新指标将这一比例提高到62.5%,提高了25%。为了评估拟议指标的更广泛适用性,我们在第二个数据集Monant Medical Misinformation上验证了我们的框架,该数据集涵盖了COVID-19以外的各种健康错误信息讨论。结果证实,先进的指标成功地进行了推广,确定了传统方法无法捕捉到的独特的有影响力的行动者。总的来说,研究结果表明,传统和新型中心性措施的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和更普遍的框架。
Analysing health misinformation with advanced centrality metrics in online social networks.
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.