# Opinion Dynamics with Backfire Effect and Biased Assimilation

## TL;DR

This paper introduces a novel opinion dynamics model that incorporates the Backfire Effect and Biased Assimilation, providing insights into polarization and consensus in social networks amidst misinformation.

## Contribution

It is the first model to explicitly incorporate the Backfire Effect into opinion dynamics, combining it with Biased Assimilation for more realistic social behavior modeling.

## Key findings

- Conditions for polarization and consensus identified
- Model predicts opinion extremity and clustering
- Theoretical and empirical analysis confirms model behavior

## Abstract

The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) \emph{Biased Assimilation}: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) \emph{Backfire Effect}: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.