Discovering Polarization Niches via Dense Subgraphs with Attractors and Repulsers.

Detecting niches of polarization in social media is a first step towards deploying mitigation strategies and avoiding radicalization. In this paper, we model polarization niches as close-knit dense communities of users, which are under the influence of some well-known sources of misinformation, and isolated from authoritative information sources. Based on this intuition we define the problem of finding a subgraph that maximizes a combination of (i) density, (ii) proximity to a small set of nodes A (named Attractors), and (iii) distance from another small set of nodes R (named Repulsers). Deviating from the bulk of the literature on detecting polarization, we do not exploit text mining or sentiment analysis, nor we track the propagation of information: we only exploit the network structure and the background knowledge about the sets A and R, which are given as input. We build on recent algorithmic advances in supermodular maximization to provide an iterative greedy algorithm, dubbed Down in the Hollow (dith), that converges fast to a near-optimal solution. Thanks to a novel theoretical upper bound, we are able to equip dith with a practical device that allows to terminate as soon as a solution with a user-specified approximation factor is found, making our algorithm very efficient in practice. Our experiments on very large networks confirm that our algorithm always returns a solution with an approximation factor better or equal to the one specified by the user, and it is scalable. Our case-studies in polarized settings, confirm the usefulness of our algorithmic primitive in detecting polarization niches.