Abstract
This research applies natural language processing, machine learning, and deep learning techniques for the real-time detection of Domestic Violence using indicators from X (Twitter) data. For a dataset of over 2.9 million tweets, the study proposes a multi-step computational pipeline comprising data gathering, preprocessing, feature engineering, and model evaluation. The study also addresses data imbalance using SMOTE and uses topic modeling and TF-IDF feature extraction for meaningful representation. The methodology integrates classic models such as Decision Trees and Naïve Bayes with contemporary architectures such as LSTM, CNN, and a hybrid CNN-LSTM model. Comparative analysis using accuracy, precision, recall, and F1-score shows LSTM to be superior to all models. The findings point to the efficacy of deep learning for detecting subtle indicators of domestic violence, and the moral responsibility that accompanies automated detection. The research offers a baseline model for leveraging AI to inform early intervention and policy development, for the creation of safer online environments, and an evidence-based community response.