Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{174158,
        author = {Dasari Sai Srujan Goud and Routhu Ravi Teja and Sukka Rohith and Dr .M V Krishna Rao},
        title = {Hate speech and offensive language detection using ml algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4406-4412},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174158},
        abstract = {The exponential growth of online social networks (OSNs) and platforms like Twitter, Facebook, and Instagram has made moderating user-generated content, especially hate speech, increasingly challenging. This paper presents a machine learning approach for real-time detection of hate speech on Twitter by leveraging natural language processing (NLP) techniques and machine learning algorithms, including Support Vector Machine (SVM) and Random Forest, to analyze lexical, syntactic, semantic, and contextual features. A comprehensive annotated dataset is used to train the models, evaluated through metrics such as precision, recall, F1 score, and accuracy. The system addresses existing limitations by offering nuanced insights into the context of hate speech, contributing to safer online environments. With enhanced model interpretability and real-time detection capabilities, this scalable solution aims to mitigate the impact of hate speech on social media platforms.},
        keywords = {Hate Speech Detection, Machine Learning, Natural Language Processing (NLP), Social Media Moderation, Real-time Detection, Twitter Analysis},
        month = {March},
        }