"There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit
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Abstract (arXiv)
Generative AI is altering how we work, learn, communicate, and participate in online communities. How might online communities be changed by generative AI? To start addressing this question, we focused on online community moderators' experiences with AI-generated content (AIGC). We performed fifteen in-depth, semi-structured interviews with moderators of Reddit communities that restrict the use of AIGC. Our study finds that rules about AIGC are motivated by concerns about content quality, social dynamics, and governance challenges. Moderators fear that, without such rules, AIGC threatens to reduce their communities' utility and social value. We find that, despite the absence of robust tools for detecting AIGC, moderators were able to somewhat limit the disruption it caused by working with their communities to clarify norms. However, moderators found enforcing AIGC restrictions challenging, as they rely on time-intensive and inaccurate detection heuristics. Our results highlight the importance of supporting community autonomy and self-determination in the face of this sudden technological change, and suggest potential design solutions that may help.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Nov 2023.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 4.
8 papers.
- 2024Examining the Prevalence and Dynamics of AI-Generated Media in Art Subredditscited by 8
- 2024ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scalecited by 5
- 2025Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformationcited by 4
- 2024Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trollingcited by 3
- 2026The Enforcement and Feasibility of Hate Speech Moderationcited by 1
- 2026PluRule: A Benchmark for Moderating Pluralistic Communities on Social Mediacited by 0
- 2026ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Imagescited by 0
- 2026Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitationscited by 0
- 2026Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations5 Oct 2026
- 2026ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images23 Sep 2026
- 2026PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media16 May 2026
- 2026The Enforcement and Feasibility of Hate Speech Moderation14 Apr 2026
- 2025Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation8 Jun 2025
- 2024Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits9 Oct 2024
- 2024Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling5 Oct 2024
- 2024ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale3 Apr 2024