Analysing Health Misinformation with Advanced Centrality Metrics in Online Social Networks

TL;DR

This paper introduces three advanced centrality metrics incorporating temporal and multilayered network dynamics to better identify influential nodes and pathways in health misinformation spread on social networks, improving intervention effectiveness.

Contribution

It proposes and validates three novel centrality metrics that enhance traditional methods by capturing dynamic, susceptibility, and multilayered network features in health misinformation analysis.

Findings

New metrics identified 24 unique influential nodes, increasing coverage by 44.83%.

Interventions using advanced metrics reduced misinformation by 62.5%.

Framework validated on diverse datasets, confirming generalisability.

Abstract

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.