From mean-field to complex topologies: network effects on the algorithmic bias model

. Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social network-ing websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the prolifera-tion 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.