The exponential growth of social media platforms has led to an increase in user- generated content, enhancing global connectivity but also facilitating the spread of harmful language, including hate speech. Addressing this issue requires robust, Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest—were tested, with Linear Regression achieving the highest accuracy automated systems for detecting and mitigating offensive content. This paper presents a comprehensive analysis of methodologies involving machine learning (ML) and natural language processing (NLP) for hate speech detection, focusing on With a focus on Twitter as a data source because of its extremely dynamic and text-rich environment, we give a thorough review of approaches using machine learning (ML) and natural language processing (NLP) for the detection of hate speech in this paper. Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest classifiers were among the algorithms that were analyzed and assessed. The ability of each of these methods to detect objectionable material in sizable datasets was evaluated. Outperforming all of these models, Linear Regression outperformed the others with an accuracy rating of 94%. The results of this study demonstrate how promising natural language processing (NLP) is for improving the dependability of online content moderation systems. Automated systems can help create safer and more welcoming digital spaces by skillfully fusing statistical learning methods with linguistic analysis. However, there are still a number of restrictions, including how to deal with linguistic ambiguity, context-specific subtleties, and the ever-changing nature of online language. These difficulties highlight the need for more research that takes into account contextual embeddings, deep learning architectures, and hybrid strategies that combine supervised and unsupervised techniques.

Social media platforms, Hate speech, Automated systems, Machine learning (ML), Natural language processing (NLP), Twitter data, Linear Regression, Hate speech detection.

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1.  Al-Makhadmeh Z, et al. Techniques in natural language processing for hate speech detection.
2. Kim Y, et al. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1746–51.
3. Martins T, et al. Character-level input models for word-level predictions.
4. Sanoussi A, et al. Multilingual hate speech detection using NLP and machine learning.
5. Rahman F, et al. Sentiment analysis for societal issues: Applications in crisis contexts.
6. Devlin J, Chang M, Lee K, Toutanova K. BERT: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint. 2018. arXiv:1810.04805.
7. Breiman L, Cutler A. Random forests. Machine Learning. 2001;45(1):5–32.
8. Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.
9. Vapnik VN. The nature of statistical learning. (No Title). 1998.
10. Mubeen M, Muskan A, Akram A, Rashid J, Alshalali TA, Sarwar N. Cyberbullying- Related Automated Hate Speech Detection on Social Media Platforms Using Stack Ensemble Classification Method. International Journal of Computational Intelligence Systems. 2025 Dec;18(1):1-24.

The exponential growth of social media platforms has led to an increase in user- generated content, enhancing global connectivity but also facilitating the spread of harmful language, including hate speech. Addressing this issue requires robust, Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest—were tested, with Linear Regression achieving the highest accuracy automated systems for detecting and mitigating offensive content. This paper presents a comprehensive analysis of methodologies involving machine learning (ML) and natural language processing (NLP) for hate speech detection, focusing on With a focus on Twitter as a data source because of its extremely dynamic and text-rich environment, we give a thorough review of approaches using machine learning (ML) and natural language processing (NLP) for the detection of hate speech in this paper. Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest classifiers were among the algorithms that were analyzed and assessed. The ability of each of these methods to detect objectionable material in sizable datasets was evaluated. Outperforming all of these models, Linear Regression outperformed the others with an accuracy rating of 94%. The results of this study demonstrate how promising natural language processing (NLP) is for improving the dependability of online content moderation systems. Automated systems can help create safer and more welcoming digital spaces by skillfully fusing statistical learning methods with linguistic analysis. However, there are still a number of restrictions, including how to deal with linguistic ambiguity, context-specific subtleties, and the ever-changing nature of online language. These difficulties highlight the need for more research that takes into account contextual embeddings, deep learning architectures, and hybrid strategies that combine supervised and unsupervised techniques.

Social media platforms, Hate speech, Automated systems, Machine learning (ML), Natural language processing (NLP), Twitter data, Linear Regression, Hate speech detection.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1.  Al-Makhadmeh Z, et al. Techniques in natural language processing for hate speech detection.
2. Kim Y, et al. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1746–51.
3. Martins T, et al. Character-level input models for word-level predictions.
4. Sanoussi A, et al. Multilingual hate speech detection using NLP and machine learning.
5. Rahman F, et al. Sentiment analysis for societal issues: Applications in crisis contexts.
6. Devlin J, Chang M, Lee K, Toutanova K. BERT: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint. 2018. arXiv:1810.04805.
7. Breiman L, Cutler A. Random forests. Machine Learning. 2001;45(1):5–32.
8. Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.
9. Vapnik VN. The nature of statistical learning. (No Title). 1998.
10. Mubeen M, Muskan A, Akram A, Rashid J, Alshalali TA, Sarwar N. Cyberbullying- Related Automated Hate Speech Detection on Social Media Platforms Using Stack Ensemble Classification Method. International Journal of Computational Intelligence Systems. 2025 Dec;18(1):1-24.