# Bonsai: Intentional and Personalized Social Media Feeds

arXiv: https://arxiv.org/abs/2509.10776 · HTML: https://mlsanity.com/paper/2509.10776/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2025.

## Built on by (most cited first, 6 of 6)

- [The Hidden Toll of Social Media News: Causal Effects on Psychosocial Wellbeing](https://mlsanity.com/paper/2601.13487/) (2026)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)
- [Algorithmic Cultivation: How Social Media Feeds Shape User Language](https://mlsanity.com/paper/2605.17010/) (2026)
- [LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems](https://mlsanity.com/paper/2606.02883/) (2026)
- [Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections](https://mlsanity.com/paper/2608.04274/) (2026)
- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

Social media feeds use predictive models to maximize engagement, often misaligning how people consume content with how they wish to. We introduce Bonsai, a system that enables people to build personalized and intentional feeds. Bonsai implements a platform-agnostic framework comprising Planning, Sourcing, Curating, and Ranking modules. This framework allows users to express their intent in natural language and exert fine-grained control over a procedurally transparent feed creation process. We evaluated the system with 15 Bluesky users in a two-phase, multi-week study. We find that participants successfully used our system to discover new content, filter out irrelevant or toxic posts, and disentangle engagement from intent, but curating intentional feeds required more effort than they are used to. Simultaneously, users sought system transparency mechanisms to effectively use (and trust) intentional, personalized feeds. Overall, our work highlights intentional feedbuilding as a viable path beyond engagement-based optimization.