  Understanding the spread of false or dangerous beliefs through a population
has never seemed so urgent. Network science researchers have often taken a page
from epidemiologists, and modeled the spread of false beliefs as similar to how
a disease spreads through a social network. However, absent from those
disease-inspired models is an internal model of an individual's set of current
beliefs, where cognitive science has increasingly documented how the
interaction between mental models and incoming messages seems to be crucially
important for their adoption or rejection. Some computational social science
modelers analyze agent-based models where individuals do have simulated
cognition, but they often lack the strengths of network science, namely in
empirically-driven network structures. We introduce a cognitive cascade model
that combines a network science belief cascade approach with an internal
cognitive model of the individual agents as in opinion diffusion models as a
public opinion diffusion (POD) model, adding media institutions as agents which
begin opinion cascades. We conduct an analysis of the cognitive cascade model
with our simple cognitive function across various graph topologies and
institutional messaging patterns. We argue from our results that
population-level aggregate outcomes of the model qualitatively match what has
been reported in COVID-related public opinion polls, and that the model
dynamics lend insights as to how to address the spread of problematic beliefs.
The overall model sets up a framework with which social science misinformation
researchers and computational opinion diffusion modelers can join forces to
understand, and hopefully learn how to best counter, the spread of
disinformation and "alternative facts."
