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Nowadays, we live in a society where people often form their opinion byaccessing and discussing contents shared on social networking websites. Whilethese platforms have fostered information access and diffusion, they representoptimal environments for the proliferation of polluted contents, which isargued to be one of the co-causes of polarization/radicalization. Moreover,recommendation algorithms - intended to enhance platform usage - are likely toaugment such phenomena, generating the so called Algorithmic Bias. In thiswork, we study the impact that different network topologies have on theformation and evolution of opinion in the context of a recent opinion dynamicmodel which includes bounded confidence and algorithmic bias. Mean-field,scale-free and random topologies, as well as networks generated by theLancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinionfragmentation/polarization and time to convergence.

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