Abstract: The proliferation of hate speech on social media has become a pressing societal concern, prompting the need for effective identification and mitigation strategies. This abstract outlines a novel approach utilizing machine learning (ML) and natural language processing (NLP) techniques to detect hate speech and assess its impact on inducing stress among users. The study focuses on the development of an ML-based model trained on a diverse dataset of social media content to accurately identify hate speech. Leveraging NLP, the model aims to comprehend linguistic nuances, context, and sentiment within textual data, enabling it to distinguish between normal discourse and potentially harmful language. Furthermore, the research extends beyond mere identification, aiming to gauge the psychological impact of hate speech by analyzing its correlation with stress levels among social media users. By employing sentiment analysis and stress identification algorithms, the study aims to quantify the emotional toll experienced by individuals exposed to such content. The abstract emphasizes the interdisciplinary nature of the research, bridging the gap between computer science, linguistics, and psychology. The proposed methodology holds promise in aiding social media platforms, policymakers, and mental health professionals in devising targeted interventions to combat hate speech and mitigate its adverse effects on users' well being. Through this holistic approach, this study endeavors to contribute to the development of proactive strategies for early detection, intervention, and support mechanisms, fostering a safer and healthier online environment for all users.

Keywords: Stressfull comments, hate speech, personal assaults, healthier online environment.

| DOI: 10.17148/IARJSET.2024.11443