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