Neural Network-Based Detection of Gender-Biased Hate Speech in Social Media Using Optimized Text Classification

This paper presents a robust neural network-based framework for detecting gender-biased hate speech in social media, leveraging optimized text classification techniques. The increasing prevalence of hate speech on platforms like Twitter poses a significant threat to digital discourse, necessitating automated detection systems capable of handling the complexities of natural language. Our proposed model achieves a state-of-the-art classification accuracy of 98.19%, surpassing conventional machine learning approaches. The methodology integrates advanced natural lan- guage processing (NLP) techniques with deep learning, employing a dataset of 5,000 tweets collected via the SNSCRAPE API. The system features a meticulously designed text preprocessing pipeline utilizing the NLTK library, vocabulary optimization through frequency-based filtering, and a custom neural network architecture enhanced with dropout regularization for improved generalization. A key contribution of this work is its ability to address three major challenges in gender-biased hate speech detection: the context-dependent nature of offensive language, extreme class imbalance in real-world datasets, and linguistic variations inherent in short-text social media content. Through extensive experimentation, our model demonstrates a 12.4% improvement in classification performance over traditional approaches such as Support Vector Machines (SVM) and Logistic Regression. Furthermore, the inclusion of an adaptive learning rate mechanism ensures stability in model convergence, while dropout regularization mitigates overfitting. This research contributes to ongoing efforts to develop au- tomated systems for hate speech detection by proposing an optimized deep learning approach capable of handling noisy, unbalanced, and context-sensitive social media data. Future work will extend this framework to multilingual settings and real-time detection applications.