# Rebalancing Social Feed to Minimize Polarization and Disagreement

arXiv: https://arxiv.org/abs/2308.14486 · HTML: https://mlsanity.com/paper/2308.14486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics](https://mlsanity.com/paper/1908.05209/) (2019)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [Users Polarization on Facebook and Youtube](https://mlsanity.com/paper/1604.02705/) (2016)
- [Balancing Information Exposure in Social Networks](https://mlsanity.com/paper/1709.01491/) (2017)
- [Polarization and Fake News: Early Warning of Potential Misinformation Targets](https://mlsanity.com/paper/1802.01400/) (2018)
- [Cascade-based Echo Chamber Detection](https://mlsanity.com/paper/2208.04620/) (2022)
- [Maximizing the Diversity of Exposure in a Social Network](https://mlsanity.com/paper/1809.04393/) (2018)
- [Optimal Engagement-Diversity Tradeoffs in Social Media](https://mlsanity.com/paper/2303.03549/) (2023)
- [Effects of Stubbornness on Opinion Dynamics](https://mlsanity.com/paper/2208.04160/) (2022)
- [Tell me something my friends do not know: Diversity maximization in social networks](https://mlsanity.com/paper/1811.10354/) (2018)
- [A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion](https://mlsanity.com/paper/2108.09865/) (2021)

## Built on by (most cited first, 2 of 2)

- [Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits](https://mlsanity.com/paper/2510.00803/) (2025)
- [Optimally Improving Cooperative Learning in a Social Setting](https://mlsanity.com/paper/2405.20808/) (2024)

## Abstract (arXiv)

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.