Cognitive cascades: How to model (and potentially counter) the spread of fake news

TL;DR

This paper introduces a cognitive cascade model that integrates individual belief cognition with network-based belief spread, providing insights into countering misinformation and aligning with observed public opinion trends.

Contribution

It combines network science and cognitive modeling to better understand and potentially counter the spread of fake news in social networks.

Findings

Model outcomes qualitatively match COVID-19 public opinion polls.

Insights into how messaging patterns influence belief cascades.

Framework for interdisciplinary research on misinformation spread.

Abstract

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."