Mass media impact on opinion evolution in biased digital environments: a bounded confidence model

People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and difusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the efects of the combination of social infuence and mass media infuence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confdence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four diferent media landscapes and found that an openminded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users' biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.