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

Abstract

The increasing prevalence of cyber-hate on online platforms poses significant threats to individuals and
society, necessitating advanced detection mechanisms. Traditional hate speech detection models often
struggle with context ambiguity, evolving linguistic patterns, and subtle hate speech variations. This
paper proposes a multi-stage machine learning and fuzzy logic-based approach to enhance the accuracy
and adaptability of cyber-hate detection. In the first stage, natural language processing (NLP) techniques
are used for text preprocessing, feature extraction, and sentiment analysis. The second stage employs
machine learning classifiers, including Support Vector Machines (SVM), Random Forest, and Deep
Learning models, to categorize content as hate or non-hate speech. Finally, a fuzzy logic-based decision
model refines classification results by handling borderline cases with linguistic uncertainty and contextual
nuances. Experimental results on benchmark hate speech datasets demonstrate that our approach
outperforms conventional models in terms of precision, recall, and F1-score, making it more effective in
identifying subtle and implicit cyber-hate speech. This research highlights the significance of integrating
machine learning and fuzzy logic for robust and scalable cyber-hate detection across multiple online
platforms.