AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content
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Abstract (arXiv)
How are Reddit communities responding to AI-generated content? We explored this question through a large-scale analysis of subreddit community rules and their change over time. We collected the metadata and community rules for over 300,000 public subreddits and measured the prevalence of rules governing AI. We labeled subreddits and AI rules according to existing taxonomies from the HCI literature and a new taxonomy we developed specific to AI rules. While rules about AI are still relatively uncommon, the number of subreddits with these rules more than doubled over the course of a year. AI rules are more common in larger subreddits and communities focused on art or celebrity topics, and less common in those focused on social support. These rules often focus on AI images and evoke, as justification, concerns about quality and authenticity. Overall, our findings illustrate the emergence of varied concerns about AI, in different community contexts. Platform designers and HCI researchers should heed these concerns if they hope to encourage community self-determination in the age of generative AI. We make our datasets public to enable future large-scale studies of community self-governance.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2024.
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Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Redditcited by 4
- 2026Understanding Risk and Dependency in AI Chatbot Use from User Discoursecited by 2
- 2026"That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Commentscited by 1
- 2026Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharingcited by 0
- 2026Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMscited by 0
- 2026"That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments10 Jun 2026
- 2026Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs3 Jun 2026
- 2026Understanding Risk and Dependency in AI Chatbot Use from User Discourse10 Feb 2026
- 2026Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing18 Jan 2026
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit8 Oct 2025