vincenzo-afk/SocialGuard-RL

SocialGuard-RL is a hackathon-tier prototype (Meta OpenEnv Hackathon) with minimal traction (1 star, 0 forks). While the concept of using RL for automated moderation is a valid research area, this specific implementation lacks the data gravity or algorithmic complexity to form a moat. It faces extreme platform domination risk; Meta, the very host of the referenced hackathon, possesses internal simulation environments and proprietary datasets that far exceed the capabilities of an open-source prototype. The project competes with established academic research in Multi-Agent Reinforcement Learning (MARL) for graph-based anomaly detection. Given its age (11 days) and lack of community engagement, it is currently a reference implementation rather than a viable tool. Frontier labs and major social platforms will continue to build these capabilities in-house using real-world user interaction data that cannot be effectively simulated by a standalone RL environment.