Design and Implementation of a Comprehensive AI-Driven NLP Framework for Classifying and Preventing Hate Speech in Social Media

Abstract

The rapid growth of social media has increased the spread of hate speech, creating challenges for online safety and communication. This research proposes an Artificial Intelligence (AI) and Natural Language Processing (NLP) based system to automatically detect and classify hate speech in social media posts. The system uses deep learning and transformer-based models to improve detection accuracy. It also applies text preprocessing techniques to handle informal social media language. The proposed model can identify different types of hate speech such as racism, sexism, religious hatred, and cyberbullying. Experimental results show better accuracy and performance compared to traditional machine learning methods for hate speech detection.

Introduction

Social media platforms have enabled rapid global communication but also amplified harmful content such as hate speech, cyberbullying, and abusive language. Traditional moderation methods like manual review and keyword-based filtering are not scalable and often fail to detect implicit or context-dependent hate speech. To address this, the paper proposes an AI-driven Natural Language Processing (NLP) framework that automatically detects and classifies hate speech in social media text.

The system uses standard NLP preprocessing (cleaning, tokenization, lemmatization) and feature extraction techniques such as TF-IDF and word embeddings. It applies machine learning models including Naive Bayes, SVM, Logistic Regression, and Random Forest, with advanced transformer-based models like BERT offering improved contextual understanding and higher accuracy. The system classifies text into three categories: hate speech, offensive language, and neutral content.

The architecture includes layers for data input, preprocessing, feature extraction, classification, probability estimation, and output generation. It is implemented in Python with a web-based interface (Streamlit) for real-time predictions.

Results show that the system is efficient and accurate, with Naive Bayes providing fast baseline performance, while advanced models improve contextual detection. Overall, the framework outperforms traditional moderation systems by improving accuracy, scalability, and real-time detection capability.

Conclusion

This paper presented a comprehensive AI-driven NLP framework for hate speech detection in social media. The proposed system uses text preprocessing, TF-IDF feature extraction, and machine learning algorithms including Naive Bayes, Logistic Regression, SVM, and Random Forest for classification. The framework effectively identifies hate speech, offensive language, and neutral content with improved contextual understanding and higher accuracy compared to traditional moderation systems. The proposed solution supports automated moderation and contributes to safer digital communication platforms.

References

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Copyright

Copyright © 2026 V Vani Tejaswini, Dr. K Srinivas, G Phani Durga Anusha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.