Title: Factors in Recommending Contrarian Content on Social Media,
Abstract: Polarization is a troubling phenomenon that can lead to societal divisions
and hurt the democratic process. It is therefore important to develop methods
to reduce it.
We propose an algorithmic solution to the problem of reducing polarization.
The core idea is to expose users to content that challenges their point of
view, with the hope broadening their perspective, and thus reduce their
polarity. Our method takes into account several aspects of the problem, such as
the estimated polarity of the user, the probability of accepting the
recommendation, the polarity of the content, and popularity of the content
being recommended.
We evaluate our recommendations via a large-scale user study on Twitter users
that were actively involved in the discussion of the US elections results.
Results shows that, in most cases, the factors taken into account in the
recommendation affect the users as expected, and thus capture the essential
features of the problem.