  Social media platforms have played a key role in weaponizing the polarization
of social, political, and democratic processes. This is, mainly, because they
are a medium for opinion formation. Opinion dynamic models are a tool for
understanding the role of specific social factors on the acceptance/rejection
of opinions because they can be used to analyze certain assumptions on human
behaviors. This work presents a framework that uses concurrent set relations as
the formal basis to specify, simulate, and analyze social interaction systems
with dynamic opinion models. Standard models for social learning are obtained
as particular instances of the proposed framework. It has been implemented in
the Maude system as a fully executable rewrite theory that can be used to
better understand how opinions of a system of agents can be shaped. This paper
also reports an initial exploration in Maude on the use of reachability
analysis, probabilistic simulation, and statistical model checking of important
properties related to opinion dynamic models.
