# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 1 Introduction  
The rapid expansion of social media has enabled rapid communication, but it has also paved the way for the widespread dissemination of hate speech. Platforms like Facebook, with millions of active users, have become breeding grounds for offensive content that targets individuals or groups based on race, religion, gender, and ethnicity [1]. Hate speech not only violates ethical and moral boundaries but also poses serious threats to social harmony and mental well-being. The anonymity provided by online platforms encourages users to engage in hate speech without fear of accountability, making its detection and moderation a critical challenge. The sheer volume of content generated on social media makes manual moderation ineffective and impractical. Automated hate speech detection systems, powered by deep learning (DL) and machine learning (ML) algorithms, have emerged as effective solutionsto recognize and filter harmful content [2]. While substantial progress has been made in English-language hate speech detection, low-resource languages such as Roman Urdu remain largely underexplored due to the lack of annotated datasets, linguistic complexity, and informal writing styles [3]. Roman Urdu, a Latin-script representation of the Urdu language, presents unique challenges in hate speech detection. Unlike standard languages with well-defined grammar and structure, Roman Urdu lacks orthographic norms, meaning that the same word can be spelled in multiple ways (e.g., \"mujue\" vs. \"mujay\" for \"me\") [4]. Additionally, code-mixing with English, phonetic variations, and informal syntax add complexity to text classification models [5]. Traditional Natural Language Processing (NLP) techniques struggle to handle such variations, necessitating more robust approaches using ML and DL. Several ML-based hate speech detection systems have been developed using classifiers such as Random Forest (RF), Naive Bayes (NB), Support Vector Machines (SVMs), k-Nearest Neighbors (KNNs), Logistic Regression (LR), and Gradient Boosting Machines (GBMs) [6]. These models rely on handcrafted features, including n-grams, TF-IDF (Term Frequency-Inverse Document Frequency), and word embeddings to recognize hate speech. While effective in some cases, these approaches often fail to capture semantic meaning, sarcasm, and implicit hate speech [7]. Deep learning models have demonstrated superior performance in hate speech classification due to their ability to learn complex linguistic patterns. Gated Recurrent Units (GRUs), Long Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Short-Term Memory (LSTM) are among the most commonly used architectures for text classification tasks [8]. These models leverage word embeddings (Word2 Vec, FastText, and GloVe) to capture contextual relationships, making them more effective than traditional ML approaches in understanding hate speech nuances [9]. While ML models are computationally efficient and interpretable, they struggle to capture deep contextual information. DL models, on the other hand, excel at learning complex patterns but require large annotated datasets and high computational power. Studies have shown that CNNs and LSTM models outperform traditional ML models, achieving accuracy scores of 95% and 96%, respectively, in hate speech detection tasks [10]. However, a hybrid approach combining both ML and DL models has shown promising results in recent research [11]. Hate speech detection is inherently subjective, as definitions of offensive content vary across cultures and contexts [12]. Automated systems must be carefully designed to avoid biases, false positives, and over-censorship while ensuring fair and accurate classification [13]. Ensuring transparency in AI-based hate speech detection models is crucial for building user trust and compliance with ethical guidelines [14]. A major limitation in Roman Urdu hate speech detection research is the lack of publicly available annotated datasets [15].