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

Nicholas Rabb,Matthias Scheutz,Jan P De Ruiter,Lenore Cowen,Marco Cremonini

doi:10.1371/journal.pone.0261811

Abstract

Understanding the spread of false or dangerous beliefs-often called misinformation or disinformation-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 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."

Highlights

Understanding the spread of false or dangerous beliefs through a population has never seemed so urgent
We note that our framework allows other resolutions of belief strength, from the discrete to approaching continuous, so we explore these alternative model choices with additional in-silico experiments with lower and higher “resolutions” of belief: with b able to take integer values between 0 and 2, 3, 5, 7, 9, 16, 32, and 64 to approach behavior over continuous beliefs
This paper lays out what we call a cognitive cascade model: a combination of an individual cognitive contagion model for identity-related belief spread embedded in a Public Opinion Diffusion (POD) model in which external, institutional agents dictate influence of internal agent beliefs

Summary

Introduction

Understanding the spread of false or dangerous beliefs through a population has never seemed so urgent. ABMs have been widely used to model social contagion effects—those which describe the process of ideas or beliefs spreading through a population [24] Such models attempt to explain possible processes underlying the spread of innovations [15, 73, 74], culture and ideology [27, 28, 72, 75,76,77], or unpopular norms [68]. There are two popular types of social contagion models used in ABMs: simple— called independent cascade—and complex contagion—which has a proportional and absolute variation Both model the spread of behaviors, norms, or ideas through a population. We will refer to behaviors or norms as “beliefs” going forward, as it is plausible to argue that both are generated by beliefs that an individual holds, explicit or otherwise

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