The drivers of online polarization: fitting models to data

TL;DR

This paper introduces a systematic method to compare opinion dynamics models with empirical social media data, validating a new model that considers human biases and algorithmic influences on online polarization.

Contribution

It develops a novel approach for quantitatively validating opinion models against real data and introduces a new model incorporating human and algorithmic factors.

Findings

Validated the model with data from multiple social media platforms

Benchmarking against existing models shows improved accuracy

Parameter space analysis aids in understanding platform characteristics

Abstract

Users online tend to join polarized groups of like-minded peers around shared narratives, forming echo chambers. The echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. The objective was to determine the key factors contributing to these phenomena and identify their interplay. However, the validation of model predictions with empirical data still displays two main drawbacks: lack of systematicity and qualitative analysis. In our work, we bridge this gap by providing a method to numerically compare the opinion distributions obtained from simulations with those measured on social media. To validate this procedure, we develop an opinion dynamic model that takes into account the interplay between human and algorithmic factors. We subject our model to empirical testing with data from diverse social media platforms and benchmark it against two state-of-the-art models. To further enhance our understanding of social media platforms, we provide a synthetic description of their characteristics in terms of the model's parameter space. This representation has the potential to facilitate the refinement of feed algorithms, thus mitigating the detrimental effects of extreme polarization on online discourse.