Hate Speech and Offensive Content: Harnessing Machine Learning for Reliable Analysis and Detection

Abstract

The escalating prevalence of hate speech on social media necessitates effective detection mechanisms to foster a safe and inclusive online community. This research paper aims to enhance hate speech detection accuracy by evaluating the performance of diverse machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). A diverse dataset comprising text samples from various online platforms, encompassing a wide spectrum of hate speech instances, was meticulously collected. The data underwent careful preprocessing involving tokenization, stemming, and stop-word removal to enhance data quality. Additionally, feature extraction techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings were employed to effectively represent the textual content. The dataset was divided into training and testing sets, and the selected machine learning algorithms were trained on the former. Fine-tuning of hyperparameters was performed using crossvalidation techniques to optimize their performance. Evaluation metrics, including accuracy, precision, recall, and F1-score, were employed to assess the models’ effectiveness. The experimental findings revealed promising outcomes for hate speech detection across all three algorithms. Notably, Count Vectorizer features demonstrated excellent performance, with Random Forest achieving an accuracy of 0.942 for binary hate speech analysis and Logistic Regression achieving an accuracy of 0.897 for multi-class hate speech analysis, followed by LR and KNN.