  Nowadays, we live in a society where people often form their opinion by
accessing and discussing contents shared on social networking websites. While
these platforms have fostered information access and diffusion, they represent
optimal environments for the proliferation of polluted contents, which is
argued to be one of the co-causes of polarization/radicalization. Moreover,
recommendation algorithms - intended to enhance platform usage - are likely to
augment such phenomena, generating the so called Algorithmic Bias. In this
work, we study the impact that different network topologies have on the
formation and evolution of opinion in the context of a recent opinion dynamic
model which includes bounded confidence and algorithmic bias. Mean-field,
scale-free and random topologies, as well as networks generated by the
Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion
fragmentation/polarization and time to convergence.
