Description:
<jats:p>Abstract: This research addresses the pressing challenge of curbing abusive language in online platforms through the implementation of advanced deep learning techniques. Focused on Natural Language Processing (NLP), this study aims to develop a robust automated censorship system capable of swiftly detecting and mitigating abusive content. By leveraging the prowess of deep learning algorithms, particularly in neural network architectures, the proposed system aims to proactively identify and censor abusive language across various online platforms. Key components involve training models to comprehend contextual nuances, enabling accurate recognition of abusive language patterns. Through this approach, the research aims to significantly contribute to online moderation mechanisms, ensuring a safer and more respectful online environment. The integration of deep learning methodologies within automated censorship systems represents a pivotal step towards mitigating the spread of abusive language, thereby fostering healthier and more inclusive online communities.</jats:p>