This study explores RoBERTa’s ability to predict emotions from social media engagement data, such as scrolling behavior and post interactions. By analyzing emotional patterns across content categories, the model aids in real-time emotion monitoring for mental health and personalized content. Findings highlight RoBERTa’s effectiveness in social media emotion detection while identifying biases in prediction. The study compares various LLMs to determine the most accurate approach for detecting emotions in online environments.