  Modern technology has drastically changed the way we interact and consume
information. For example, online social platforms allow for seamless
communication exchanges at an unprecedented scale. However, we are still
bounded by cognitive and temporal constraints. Our attention is limited and
extremely valuable. Algorithmic personalisation has become a standard approach
to tackle the information overload problem. As result, the exposure to our
friends' opinions and our perception about important issues might be distorted.
However, the effects of algorithmic gatekeeping on our hyper-connected society
are poorly understood. Here, we devise an opinion dynamics model where
individuals are connected through a social network and adopt opinions as
function of the view points they are exposed to. We apply various filtering
algorithms that select the opinions shown to users i) at random ii) considering
time ordering or iii) their current beliefs. Furthermore, we investigate the
interplay between such mechanisms and crucial features of real networks. We
found that algorithmic filtering might influence opinions' share and
distributions, especially in case information is biased towards the current
opinion of each user. These effects are reinforced in networks featuring
topological and spatial correlations where echo chambers and polarisation
emerge. Conversely, heterogeneity in connectivity patterns reduces such
tendency. We consider also a scenario where one opinion, through nudging, is
centrally pushed to all users. Interestingly, even minimal nudging is able to
change the status quo moving it towards the desired view point. Our findings
suggest that simple filtering algorithms might be powerful tools to regulate
opinion dynamics taking place on social networks
