TY  - EJOU
AU  - Hussain, Ayaz 
AU  - Hayat, Asad 
AU  - Hasnain, Muhammad 

TI  - Using Hate Speech Detection Techniques to Prevent Violence and Foster Community Safety
T2  - Journal on Artificial Intelligence

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-003X

AB  - Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the model. The experimental results indicate that LR and RF achieved good performance compared to other models, with LR achieving the highest testing accuracy of 93.66 and RF providing good general performance. These results demonstrate that the state-of-the-art applications of deep learning models are surpassed by optimized, and traditional machine learning models for hate speech detection. The research also highlights the need for constant re-adaptation to a linguistic shift and new forms of intolerance, emphasizing the interest of machine learning to support the actions against prejudice and social injustice in digital environments. This study benchmarks optimized machine learning algorithms for hate speech detection, demonstrating that traditional models can rival deep learning performance.
KW  - Hate speech; logistic regression; machine learning; random forest; violence

DO  - 10.32604/jai.2025.071933