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) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench people in their stances, making their opinions more extreme instead of moderating them. To the best of our knowledge, this is the first DeGroot-type opinion formation 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.
Highlights
Recent years have seen an increasing amount of attention from the computational social science in the study of opinion formation and polarization over social networks, with applications ranging from politics to brand perception [1,2,3]
First we investigate the dynamics of opinions for a single agent in a fixed environment, and secondly we study the dynamics of polarization for all nodes in a connected social network
We investigate the following question as a potential application of our model on opinion manipulation: how will the opinion dynamics be influenced by edge addition or deletion in networks? We use the Karate network to study this question experimentally
Summary
Recent years have seen an increasing amount of attention from the computational social science in the study of opinion formation and polarization over social networks, with applications ranging from politics to brand perception [1,2,3]. Much of this research leverages preexisting opinion formation models that have been studied for decades [4, 5]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Those beliefs more strongly than information that contradicts them [11]. The backfire effect may help explain the emergence of polarization It has so far been overlooked by existing opinion formation models. We propose the BEBA model of opinion formation, which accounts for both the Backfire Effect and Biased Assimilation. To the best of our knowledge BEBA is the first DeGroot-type opinion formation model that incorporates the Backfire Effect. We empirically evaluate, on real and synthetic data, the influence of the entrenchment parameter, the initial opinions, and the network topology, on the opinion dynamics of BEBA
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