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.Author summary: False health information on social media can spread rapidly during crises, influencing how people think, feel, and behave. This can lead to harmful consequences for public health. Our study investigates how such misinformation travels through online networks and which users are most influential in spreading it. Traditional approaches often identify influencers only on the basis of how many connections they have. However, this overlooks individuals who are vulnerable to misinformation or whose influence builds up gradually. We developed three new tools that capture not only structural importance, but also behavioural susceptibility and time-based influence. These methods were applied to real-world data covering both COVID-19 and broader medical misinformation. The results reveal that some users act as persistent spreaders of misinformation, while others are highly exposed and more likely to believe and share false content. These patterns are often missed/overlooked by conventional techniques. The findings support the need for more targeted strategies from health organisations and platforms to identify and limit the spread of misleading health claims. Using dynamic and context-aware metrics offers a clearer picture of how misinformation operates in complex social networks and may help guide more effective interventions.