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Generative AI has begun to alter how we work, learn, communicate, and participate in online communities. How might our 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 foolproof tools for detecting AIGC, moderators were able to somewhat limit the disruption caused by this new phenomenon by working with their communities to clarify norms. However, moderators found enforcing AIGC restrictions challenging, and had to rely on time-intensive and inaccurate detection heuristics in their efforts. 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.
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This content will become publicly available on June 7, 2026
Reddit Rules and Rulers: Quantifying the Link Between Rules and Perceptions of Governance Across Thousands of Communities
Rules are a critical component of the functioning of nearly every online community, yet it is challenging for community moderators to make data-driven decisions about what rules to set for their communities. The connection between a community's rules and how its membership feels about its governance is not well understood. In this work, we conduct the largest-to-date analysis of rules on Reddit, collecting a set of 67,545 unique rules across 5,225 communities which collectively account for more than 67% of all content on Reddit. More than just a point-in-time study, our work measures how communities change their rules over a 5+ year period. We develop a method to classify these rules using a taxonomy of 17 key attributes extended from previous work. We assess what types of rules are most prevalent, how rules are phrased, and how they vary across communities of different types. Using a dataset of communities' discussions about their governance, we are the first to identify the rules most strongly associated with positive community perceptions of governance: rules addressing who participates, how content is formatted and tagged, and rules about commercial activities. We conduct a longitudinal study to quantify the impact of adding new rules to communities, finding that after a rule is added, community perceptions of governance immediately improve, yet this effect diminishes after six months. Our results have important implications for platforms, moderators, and researchers. We make our classification model and rules datasets public to support future research on this topic.
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- Award ID(s):
- 1901386
- PAR ID:
- 10637846
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the International AAAI Conference on Web and Social Media
- Volume:
- 19
- ISSN:
- 2162-3449
- Page Range / eLocation ID:
- 1098 to 1121
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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