R2ban

R2ban is a modular framework discussed in the context of online moderation that envisions a rule-based approach to sanctioning and excluding users from digital communities. It is described as combining automated decision logic with human oversight to improve consistency and transparency in bans and related actions.

The architecture, as outlined in literature and pilot studies, typically includes a policy engine, a sanctioning core, an appeal and review workflow, and an auditing layer. The policy engine interprets defined rules and signals from user behavior, content context, and platform-specific norms. The sanctioning core applies penalties to accounts, IPs, or devices according to the severity and recurrence of violations. An appeal subsystem allows users to contest decisions, triggering human review when necessary. The auditing layer records decisions, signals, and rationales to support accountability and post-hoc analysis.

R2ban supports a range of sanctions, from warnings and temporary restrictions to longer bans, with the possibility of reversible or escalated penalties based on ongoing behavior. It is generally designed to be deployed as a standalone service or integrated into existing moderation pipelines, emphasizing modularity, configurability, and auditability. Privacy considerations, data minimization, and secure handling of user data are commonly highlighted aspects.

Reception and use vary; some projects emphasize the potential for consistent rule application and faster response times, while critics point to risks of overreach, false positives, and the need for robust human oversight. Proponents stress the importance of transparent rules, ongoing evaluation, and user-friendly appeal processes.