# Content-Agnostic Moderation for Stance-Neutral Recommendation

arXiv: https://arxiv.org/abs/2405.18941 · HTML: https://mlsanity.com/paper/2405.18941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 14 of 14)

- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [The closed loop between opinion formation and personalised recommendations](https://mlsanity.com/paper/1809.04644/) (2018)
- [Designing Recommender Systems to Depolarize](https://mlsanity.com/paper/2107.04953/) (2021)
- [Modelling Opinion Dynamics in the Age of Algorithmic Personalisation](https://mlsanity.com/paper/1811.03341/) (2018)
- [A Bias Aware News Recommendation System](https://mlsanity.com/paper/1803.03428/) (2018)
- [Opinion Dynamics with Backfire Effect and Biased Assimilation](https://mlsanity.com/paper/1903.11535/) (2019)
- [Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns](https://mlsanity.com/paper/2402.15013/) (2024)
- [The Limits of Popularity-Based Recommendations, and the Role of Social Ties](https://mlsanity.com/paper/1607.04263/) (2016)
- [Evolution of Filter Bubbles and Polarization in News Recommendation](https://mlsanity.com/paper/2301.10926/) (2023)
- [Opinion influence and evolution in social networks: a Markovian agents model](https://mlsanity.com/paper/1711.09802/) (2017)
- [Topic-Centric Explanations for News Recommendation](https://mlsanity.com/paper/2306.07506/) (2023)

## Built on by (most cited first, 1 of 1)

- [Content Fuzzing for Escaping Information Cocoons on Digital Social Media](https://mlsanity.com/paper/2604.05461/) (2026)

## Abstract (arXiv)

Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While (content-aware) moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and of information. To address this concern, we propose and explore the feasibility of content-agnostic moderation as an alternative approach for reducing polarization. Content-agnostic moderation does not rely on the actual content being moderated, arguably making it less prone to forms of censorship. We establish theoretically that content-agnostic moderation cannot be guaranteed to work in a fully generic setting. However, we show that it can often be effectively achieved in practice with plausible assumptions. We introduce two novel content-agnostic moderation methods that modify the recommendations from the content recommender to disperse user-item co-clusters without relying on content features. To evaluate the potential of content-agnostic moderation in controlled experiments, we built a simulation environment to analyze the closed-loop behavior of a system with a given set of users, recommendation system, and moderation approach. Through comprehensive experiments in this environment, we show that our proposed moderation methods significantly enhance stance neutrality and maintain high recommendation quality across various data scenarios. Our results indicate that achieving stance neutrality without direct content information is not only feasible but can also help in developing more balanced and informative recommendation systems without substantially degrading user engagement.