# Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor

arXiv: https://arxiv.org/abs/2305.10374 · HTML: https://mlsanity.com/paper/2305.10374/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2023.

## Built on by (most cited first, 18 of 18)

- [MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations](https://mlsanity.com/paper/2504.07830/) (2025)
- [Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation](https://mlsanity.com/paper/2410.23448/) (2024)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [MisinfoEval: Generative AI in the Era of "Alternative Facts"](https://mlsanity.com/paper/2410.09949/) (2024)
- [Supporting Human Raters with the Detection of Harmful Content using Large Language Models](https://mlsanity.com/paper/2406.12800/) (2024)
- [AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection](https://mlsanity.com/paper/2406.08080/) (2024)
- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)
- [Value Alignment of Social Media Ranking Algorithms](https://mlsanity.com/paper/2509.14434/) (2025)
- [Mitigating Text Toxicity with Counterfactual Generation](https://mlsanity.com/paper/2405.09948/) (2024)
- [With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems](https://mlsanity.com/paper/2603.28476/) (2026)
- [Safe Generative Chats in a WhatsApp Intelligent Tutoring System](https://mlsanity.com/paper/2407.04915/) (2024)
- [The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://mlsanity.com/paper/2506.00583/) (2025)
- [Age Matters: Analyzing Age-Related Discussions in App Reviews](https://mlsanity.com/paper/2601.21605/) (2026)
- [IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language](https://mlsanity.com/paper/2604.16654/) (2026)
- [Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaboration](https://mlsanity.com/paper/2604.17459/) (2026)
- [Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework](https://mlsanity.com/paper/2605.01416/) (2026)
- [When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts](https://mlsanity.com/paper/2606.00046/) (2026)
- [Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections](https://mlsanity.com/paper/2608.04274/) (2026)

## Abstract (arXiv)

Social media platforms moderate content for each user by incorporating the outputs of both platform-wide content moderation systems and, in some cases, user-configured personal moderation preferences. However, it is unclear (1) how end users perceive the choices and affordances of different kinds of personal content moderation tools, and (2) how the introduction of personalization impacts user perceptions of platforms' content moderation responsibilities. This paper investigates end users' perspectives on personal content moderation tools by conducting an interview study with a diverse sample of 24 active social media users. We probe interviewees' preferences using simulated personal moderation interfaces, including word filters, sliders for toxicity levels, and boolean toxicity toggles. We also examine the labor involved for users in choosing moderation settings and present users' attitudes about the roles and responsibilities of social media platforms and other stakeholders towards moderation. We discuss how our findings can inform design solutions to improve transparency and controllability in personal content moderation tools.