# Factors in Recommending Contrarian Content on Social Media

## TL;DR

This paper presents a new algorithmic approach to reduce social media polarization by recommending contrarian content, considering user and content attributes, validated through a large-scale Twitter user study.

## Contribution

It introduces a novel recommendation algorithm that incorporates multiple factors to effectively expose users to diverse viewpoints, aiming to decrease polarization.

## Key findings

- Factors influence user acceptance as expected
- Recommendations effectively broaden perspectives in most cases
- Method captures key features of polarization reduction

## 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.