  In recent decades, the massification of online social connections has made
information globally accessible in a matter of seconds. Unfortunately, this has
been accompanied by a dramatic surge in extreme opinions, without a clear
solution in sight. Using a model performing probabilistic inference in
large-scale loopy graphs through exchange of messages between nodes, we show
how circularity in the social graph directly leads to radicalization and the
polarization of opinions. We demonstrate that these detrimental effects could
be avoided if the correlations between incoming messages could be decreased.
This approach is based on an extension of Belief Propagation (BP) named
Circular Belief Propagation (CBP) that can be trained to drastically improve
inference within a cyclic graph. CBP was benchmarked using data from Facebook
and Twitter. This approach could inspire new methods for preventing the viral
spreading and amplification of misinformation online, improving the capacity of
social networks to share knowledge globally without resorting to censorship.
