  Priority users (e.g., verified profiles on Twitter) are social media users
whose content is promoted by recommendation algorithms. However, the impact of
this heterogeneous user influence on opinion dynamics, such as polarization
phenomena, is unknown. We conduct a computational mechanistic investigation of
such consequences in a stylized setting. First, we allow priority users, whose
content has greater reach (similar to algorithmic boosting), into an opinion
model on adaptive networks. Then, to exploit this gain in influence, we
incorporate stubborn user behavior, i.e., zealot users who remain committed to
opinions throughout the dynamics. Using a novel measure of echo chamber
formation, we find that prioritizing users can inadvertently reduce
polarization if they post according to the same rule but sharpen echo chamber
formation if they behave heterogeneously. Moreover, we show that a minority of
extremist ideologues (i.e., users who are both stubborn and priority) can push
the system into a transition from consensus to polarization with echo chambers.
Our findings imply that the implementation of the platform's prioritization
policy should be carefully monitored in order to ensure there is no abuse of
users with extra influence.

Social and Information Networks;Physics and Society