Social media platforms like Reddit, YouTube, and Instagram amplify rapid dissemination of negative sentiment, potentially causing harm and fostering extremist discourse. This paper addresses the NLP challenge of predicting sudden spikes in negative sentiment by fine-tuning multilingual transformer models. We present a structured pipeline emphasizing linguistic feature extraction and temporal modeling. Our experimental results, obtained from extensive Reddit, YouTube, and Instagram data, demonstrate improved forecasting accuracy over baseline methods. Ethical considerations and implications for deployment in social media moderation are thoroughly discussed. The system includes user-centric interactive features such as real-time filtering dashboards, customizable negativity thresholds, and forecasting analytics, providing actionable insights for preventative content moderation. Given its real-time deployment potential and cross-platform applicability, our system offers actionable insights for proactive content moderation.