  Polarization is an increasingly worrying phenomenon within social media.
Recent work has made it possible to detect and even quantify polarization.
Nevertheless, the few existing metrics, although defined in a continuous space,
often lead to a unimodal distribution of data once applied to users'
interactions, making the distinction between polarized and non-polarized users
difficult to draw. Furthermore, each metric relies on a single factor and does
not reflect the overall user behavior. Modeling polarization in a single form
runs the risk of obscuring inter-individual differences. In this paper, we
propose to have a deeper look at polarized online behaviors and to compare
individual metrics. We collected about 300K retweets from 1K French users
between January and July 2022 on Twitter. Each retweet is related to the highly
controversial vaccine debate. Results show that a multi-factorial analysis
leads to the identification of distinct and potentially explainable behavioral
classes. This finer understanding of behaviors is an essential step to adapt
news recommendation strategies so that no user gets locked into an echo chamber
or filter bubble.
