## 2 Literature Review  
The identification of profane and hateful messages has become a key research topic in the domain of natural language processing (NLP). Multiple approaches and methods (traditional machine learning, deep learning, transformer models, etc.) have been examined to solve such challenges in different languages. The introduction of transformer architectures has shown tremendous progress in detection accuracy, even for resource-limited languages that suffer from a lack of computational resources and labeled datasets. In the early works of Arabic hate speech detection, BERT-based models were utilized, which achieved good results and illustrated how fine-tuned smaller models such as ABMM (Arabic BERT-Mini Model) can increase detection efficiency and decrease computational costs [13]. A newer transformer model was created using an improved RoBERTa-based model coupled with GloVe embeddings, within which cyberbullying detection results significantly improved [14]. Building upon this, researchers have examined how the inclusion of emojis and sentiment analysis in specific Arabic Twitter datasets can enhance classification performance [15]. Hybrid models have also been studied in multilingual hate speech detection. Addressing the abovementioned issues so as to achieve better detection in Turkish social media content, researchers proposed SO-HATRED, a hybrid approach combining ensemble deep learning models on BERT and clustered-graph networks [16]. A similar study developed HateDetector, a cross-lingual approach to enlist hate speech analysis in multilingual online social networks using deep feature extraction methods [17]. Although research related to Urdu hate speech detection is still limited, progress has been made. A transfer learning model, UHATED, also utilizes various pre-trained models to effectively classify hate speech in Urdu-based datasets, showcasing the adaptability of pre-trained models, especially for low-resource languages [18]. Regarding deep feature extraction development for GCNs in social media troll detection, in direct parallel, [19] proposed to gain potential performance improvement in upcoming MTL models by utilizing GCNs to successfully extract deep features from social media users marked as trolls. Another hybrid method that combines semantic compression and Support Vector Machines (SVMs) focuses on filtering troll threat sentences, highlighting the role of feature selection in enhancing detection capabilities [20]. The transformer-based models have also helped improve the performance of hate speech classification in the case of Roman Urdu. [21] utilized transformer-based architectures fine-tuned for cybersecurity tasks and a notable enhancement in the classification accuracy for offensive language in the Roman Urdu datasets. A third study concentrated on cross-lingual learning methods, with implications for leveraging multilingual models to detect hate speech within linguistic communities [22]. Beyond just model performance, previous research has explored the broader psychological and societal impacts of online hate speech. Meta-analyses on cyber victimization of adolescents indicate a strong relationship between online violence and internalizing/externalizing behavioral problems [23]. More recently, extensive surveys on methodologies for hate speech detection have highlighted the progress of automatic techniques to classify text as hate speech, acknowledging the significance of dataset quality, feature engineering, and model interpretability [24]. Also, preprocessing techniques are one of the important factors in offensive language detection. It has been proven in previous works that preprocessing of Arabic text, including practical measures like removing diacritics and script normalization, enhances model performance in hate speech and offensive content classification [25]. Models like G-BERT, which are transformer-based and specialized for classifying Bengali text, are more efficient in identifying offensive speech on platforms like Facebook and Twitter [26].