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We investigate opinion dynamics and information spreading on networks underthe influence of content filtering technologies. The filtering mechanism,present in many online social platforms, reduces individuals' exposure todisagreeing opinions, producing algorithmic bias. We derive evolution equationsfor global opinion variables in the presence of algorithmic bias, networkcommunity structure, noise (independent behavior of individuals), and pairwiseor group interactions. We consider the case where the social platform shows apredilection for one opinion over its opposite, unbalancing the dynamics infavor of that opinion. We show that if the imbalance is strong enough, it maydetermine the final global opinion and the dynamical behavior of thepopulation. We find a complex phase diagram including phases of coexistence,consensus, and polarization of opinions as possible final states of the model,with phase transitions of different order between them. The fixed pointstructure of the equations determines the dynamics to a large extent. We focuson the time needed for convergence and conclude that this quantity varieswithin a wide range, showing occasionally signatures of critical slowing downand meta-stability.

Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance