Rebalancing Social Feed to Minimize Polarization and Disagreement
Federico Cinus Aristides Gionis Francesco Bonchi
Social media have great potential for enabling public discourse on important
societal issues. However, adverse effects, such as polarization and echo
chambers, greatly impact the benefits of social media and call for algorithms
that mitigate these effects. In this paper, we propose a novel problem
formulation aimed at slightly nudging users' social feeds in order to strike a
balance between relevance and diversity, thus mitigating the emergence of
polarization, without lowering the quality of the feed. Our approach is based
on re-weighting the relative importance of the accounts that a user follows, so
as to calibrate the frequency with which the content produced by various
accounts is shown to the user. We analyze the convexity properties of the
problem, demonstrating the non-matrix convexity of the objective function and
the convexity of the feasible set. To efficiently address the problem, we
develop a scalable algorithm based on projected gradient descent. We also prove
that our problem statement is a proper generalization of the undirected-case
problem so that our method can also be adopted for undirected social networks.
As a baseline for comparison in the undirected case, we develop a semidefinite
programming approach, which provides the optimal solution. Through extensive
experiments on synthetic and real-world datasets, we validate the effectiveness
of our approach, which outperforms non-trivial baselines, underscoring its
ability to foster healthier and more cohesive online communities.