mlsanityData to 14 Oct 2026

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

arXiv 2305.10374 ↗First posted 17 May 2023cs.HCcited by 18

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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.

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

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Citing papers per year: 2024: 7; 2025: 4; 2026: 7.

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  1. 2025MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulationscited by 27
  2. 2024Venire: A Machine Learning-Guided Panel Review System for Community Content Moderationcited by 7
  3. 2024Steering AI-Driven Personalization of Scientific Text for General Audiencescited by 7
  4. 2024MisinfoEval: Generative AI in the Era of "Alternative Facts"cited by 6
  5. 2024Supporting Human Raters with the Detection of Harmful Content using Large Language Modelscited by 5
  6. 2024AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detectioncited by 4
  7. 2025Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformationcited by 4
  8. 2025Value Alignment of Social Media Ranking Algorithmscited by 4
  9. 2024Mitigating Text Toxicity with Counterfactual Generationcited by 2
  10. 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
  11. 2024Safe Generative Chats in a WhatsApp Intelligent Tutoring Systemcited by 0
  12. 2025The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderationcited by 0
6 more
  1. 2026Age Matters: Analyzing Age-Related Discussions in App Reviewscited by 0
  2. 2026IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Languagecited by 0
  3. 2026Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaborationcited by 0
  4. 2026Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Frameworkcited by 0
  5. 2026When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shortscited by 0
  6. 2026Compass: Continuously Aligning Social Media Feeds via In-Situ Reflectionscited by 0