  Users polarization and confirmation bias play a key role in misinformation
spreading on online social media. Our aim is to use this information to
determine in advance potential targets for hoaxes and fake news. In this paper,
we introduce a general framework for promptly identifying polarizing content on
social media and, thus, "predicting" future fake news topics. We validate the
performances of the proposed methodology on a massive Italian Facebook dataset,
showing that we are able to identify topics that are susceptible to
misinformation with 77% accuracy. Moreover, such information may be embedded as
a new feature in an additional classifier able to recognize fake news with 91%
accuracy. The novelty of our approach consists in taking into account a series
of characteristics related to users behavior on online social media, making a
first, important step towards the smoothing of polarization and the mitigation
of misinformation phenomena.
