Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. 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 show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. 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".
@article{rabbetal22plosone,
title={Cognitive Contagion: How to model (and potentially counter) the spread of fake news},
author={Rabb, Nicholas and Cowen, Lenore and de Ruiter, Jan P. and Scheutz,
Matthias},
year={2022},
journal={PLOS ONE},
url={https://hrilab.tufts.edu/publications/rabbetal22plosone.pdf}
doi={10.1371/journal.pone.0261811}
}