• Indu bala
• Dr.Ikvinderpal SIngh

Hate speech on social media platforms poses significant challenges in maintaining a safe and inclusive online environment. Automated hate speech detection using machine learning techniques has emerged as a promising solution. This paper presents a comparative study of three popular machine learning algorithms: Support Vector Machines (SVM), Random Forest, and Logistic Regression, for hate speech detection. Each algorithm is implemented and trained using the preprocessed data and hyper parameter tuning is performed to optimize their performance. Evaluation metrics such as accuracy, precision, recall, and F1-score are employed to measure the effectiveness of the models. The comparative study's contributions lie in its performance evaluation, methodological guidance, practical implementation insights, dataset considerations, and insights for model selection. Overall, this comparative study advances the understanding of hate speech detection techniques and provides guidance for selecting appropriate machine learning algorithms in real-world applications.

Machine learning; Fake news; Support vector machine, Random forest; Logistic regression; Social media;

"Comparative Study of Machine Learning Techniques for Hate Speech Detection on Social Media Platforms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.g216-g224, January-2023, Available :http://www.jetir.org/papers/JETIR2301631.pdf

"Comparative Study of Machine Learning Techniques for Hate Speech Detection on Social Media Platforms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppg216-g224, January-2023, Available at : http://www.jetir.org/papers/JETIR2301631.pdf