Online Vaccination Views via Network Analysis

Title: Modelling and Predicting Online Vaccination Views Using Bow-tie Structure -- network analysis, machine learning & mechanistic simulation
Speaker : Yueting Han (Mathsys, Warwick)
Social media has become increasingly important in shaping public vaccination views, especially since the COVID-19 outbreak. In the realm of online social networks, this paper explores a more nuanced division of roles each user plays in information flow, going beyond the “creator-receiver” dynamics through the lens of “bow-tie structure”.The dataset we work on describes the information exchange among anti-vaccination, pro-vaccination, and neutral Facebook pages, covering the period before and during the initial stage of COVID-19. In our research, we consistently observe statistically significant bow-tie structures with different dominant components for each vaccination group over time. We further investigate changes in opinions over time, as measured by fan count variations, using agent-based simulations and machine learning models. Across both methods, accounting for bow-tie decomposition better reflects information flow differences among vaccination groups and improves our opinion dynamics prediction results. The modelling frameworks we consider can be applied to any multi-stance temporal network and could form a basis for exploring opinion dynamics using bow-tie structure in a wide range of applications.
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