Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epidemiological approach
Journal: Social Network Analysis and Mining
Year: 2025

Abstract The spread of narratives online has been extensively studied. However, prior research typically relies on metadata and often overlooks the dynamics of post stances. In this paper, we introduce a novel stance-based epidemiological model, which explicitly incorporates the stance of posts—a critical element often ignored by models that focus solely on isolated narratives. Our model, $$SEI_A I_D Z$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml