Hate Speech Detection Using Optimized Feature Representation via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO). S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power. The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English dataset, significantly reducing hate speech and toxicity. Overall, the enhanced algorithms improve accuracy and efficiency, offering an effective alternative to costly deep learning models even with noisy and unbalanced data.