# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 1 Introduction
Most existing datasets are either small, imbalanced, or insufficiently labeled, affecting model performance. In this study, we address this gap by collecting a large-scale, annotated dataset of Roman Urdu Facebook comments and utilizing it for training and evaluation of various ML and DL models [16]. Before applying ML and DL models, raw data must undergo preprocessing, including tokenization, stopword removal, stemming, lemmatization, and text vectorization [17]). Since Roman Urdu text contains spelling variations and non-standard expressions, word embeddings such as FastText and Word2 Vec are used to improve feature representation [18]. Proper data preprocessing is essential for improving classification accuracy and reducing noise in textual data. Transfer learning, where models pre-trained on large corpora are fine-tuned for specific tasks, has been widely used in hate speech detection [19]. Although transformer-basedarchitectures like BERT have shown state-of-the-art results, this study focuses on CNNs, LSTM models, and GRUs due to their computational efficiency and interpretability [20]. Future research could explore the integration of transformer-based models with existing approaches for enhanced hate speech detection in Roman Urdu. Given the challenges described above, we pose the the following research questions:  
* RQ1: Can the machine learning and deep learning models successfully identify hate speech in Roman Urdu language despite spelling variations and code-mixing? * RQ2: Which feature representation technique and classifier (i.e., ML/DL or both) combination can achieve the highest performance for Roman Urdu hate speech detection? * RQ3: How does the use of deep contextual embeddings (FastText and Word2 Vec) influence the classification accuracy on different architectures? These inquiries frame our conjecture that hybrid systems that exploit both the interpretability of ML and contextual learning capabilities of DL are going to dominate dedicated methodologies, especially in low-resource and linguistically informal settings. This research contributes to the field of Roman Urdu hate speech detection by  
* Developing a large-scale annotated dataset from Facebook comments;
* Comparing six ML models (LR, SVM, RF, NB, KNN, GBM) and four DL models (CNN, RNN, LSTM, GRU);
* Demonstrating that CNNs and LSTM models outperform other models, achieving 95.1% and 96.2% accuracy, respectively;
* Providing insights into preprocessing techniques and feature selection for non-standardized languages;
* Discussing ethical considerations and challenges in hate speech detection. The rest of the paper is organized as follows: Section 2 includes a literature review related to existing hate speech detection techniques and their challenges in Roman Urdu. Section 3 describes the methodology, such as dataset collection, preprocessing methods, and model implementation. Experimental results, analysis of model performance, and error evaluation are provided in Section 4. Finally, Section 5 concludes the paper and provides some potential future research directions.