AN AI – DRIVEN FRAMEWORK FOR DETECTING EXPLICIT AND IMPLICIT ABUSIVE COMMENTS IN ONLINE SOCIAL MEDIA

The rapid growth of online social media has led to a significant increase in abusive and harmful interactions, making automated content moderation in important research challenge. Although existing machine learning and deep learning techniques have achieved promising results in detecting explicit abusive comments, they often struggle to identify implicit abusive comments expressed through subtle, indirect or context – dependent language. This limitation reduces the effectiveness of current moderation systems and allows harmful content to remain undetected. The proposed research aims to develop an AI – Driven framework for detecting both explicit and implicit abusive comments by comparatively evaluating multiple machine learning and deep learning models. The research will involve collecting benchmark datasets, preprocessing textual data, training and evaluating different models using standard performance metrics, and identifying the most effective approach for abusive comment detection. The selected model will be further validated using real world online comments to assess its practical applicability in intelligent content moderation. The proposed framework is expected to improve the detection of subtle abusive language, support safer online interactions, and provide valuable insights for enhancing automated moderations systems across digital platforms.