  Online social networks provide users with unprecedented opportunities to
engage with diverse opinions. At the same time, they enable confirmation bias
on large scales by empowering individuals to self-select narratives they want
to be exposed to. A precise understanding of such tradeoffs is still largely
missing. We introduce a social learning model where most participants in a
network update their beliefs unbiasedly based on new information, while a
minority of participants reject information that is incongruent with their
preexisting beliefs. This simple mechanism generates permanent opinion
polarization and cascade dynamics, and accounts for the aforementioned tradeoff
between confirmation bias and social connectivity through analytic results. We
investigate the model's predictions empirically using US county-level data on
the impact of Internet access on the formation of beliefs about global warming.
We conclude by discussing policy implications of our model, highlighting the
downsides of debunking and suggesting alternative strategies to contrast
misinformation.
