User-Centric Toxicity Detection with Multi-Modal Spiking Neural Networks and Policy Optimization

Toxic interactions in online platforms pose significant challenges to user safety, inclusivity, and trust. While recent approaches have explored multi-modal and deep learning solutions, most fail to adapt to evolving user expectations and contextual nuances. This paper introduces a user-centric framework for toxicity detection that fuses text, audio, and visual data through Convolutional Spiking Neural Networks (CSNNs), optimized using a policy-gradient reinforcement learning strategy.By integrating late fusion mechanisms and spiking neurons, the system models both temporal and semantic aspects of communication. Policy optimization, specifically via Proximal Policy Optimization (PPO), enables dynamic adaptation based on user feedback and behavioral patterns, reducing overgeneralization and increasing contextual sensitivity. Experiments on the ToxVidLM dataset demonstrate improved accuracy, robustness, and user-aligned detection performance compared to state-of-the-art models.This work offers a scalable, real-time architecture tailored for safety-sensitive environments such as live streaming, virtual meetings, and online gaming, marking a step toward intelligent, user-aware toxicity moderation tools.