  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.
