A MULTI-STAGE MACHINE LEARNING AND FUZZY APPROACH TO CYBER-HATE DETECTION

Abstract

The rapid growth of online social platforms has increased the prevalence of cyber-hate, including hate speech, abusive language, offensive content, and discriminatory expressions targeting individuals or groups based on race, religion, gender, ethnicity, or other social characteristics. Such harmful content negatively impacts social harmony, mental well-being, and online community engagement. Traditional keyword-based detection methods often struggle to accurately identify the complex linguistic patterns and contextual nuances associated with cyber-hate. This paper presents a multi-stage machine learning and fuzzy logic approach for cyber-hate detection. The proposed framework employs Natural Language Processing techniques for text preprocessing, feature extraction, and semantic analysis, followed by machine learning algorithms for initial classification of online content. A fuzzy inference system is subsequently utilized to handle uncertainty, ambiguity, and varying degrees of hate intensity within textual data. The multi-stage architecture enhances detection accuracy by combining the predictive capabilities of machine learning with the interpretability and flexibility of fuzzy logic. Experimental analysis demonstrates that the proposed approach effectively identifies cyber-hate content, reduces false classifications, and improves overall detection performance. The developed framework provides an intelligent and scalable solution for promoting safer and more inclusive online communication environments.