2017/05/16 by Kiran Garimella, Gianmarco De Francisci Morales, Garimella, Kiran +5 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #Social Media and Politics #cs.SI
paper · pdf · doi:10.48550/arxiv.1705.06597
accepted as a short paper at ACM WebScience 2017. arXiv admin note: substantial text overlap with arXiv:1703.10934
arxiv created 2017/05/16 · arxiv updated 2017/05/19
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.