## 6 Future Directions  
Research can be conducted in the future to include data from other social media platforms, rather than limiting this study to YoYouTube, which will help in better gen  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Language(s)** & **Dataset Description** & **Model(s) Used** & **F1-Score** \\ \hline
[31] & Urdu & 36K Urdu tweets (hate, offensive, neither) & FastText, & \\  & & 3 offensive, neither) & XLM-RoBERTa, & 0.94 \\  & & & ULMFiT, XGBost & \\
[31] & Bulgarian, Hindi, & Hope speech detection & Fine-tuned mBERT & 0.74 \\  & Spanish & from social media & Hybrid DL + ML with & 0.95 \\
[32] & Roman Urdu & RUHSOLD corpora & M-BERT & \\  & & & RUHSOLD & \\
[33] & Roman Urdu & (10,012 Roman Urdu tweets) & CNN-gram, transfer learning models & 0.88 \\  & & & Hate speech datasets & \\
[35] & English & with domain & BERT, domain-specific & \\  & & embeddings & Bi-LSTM & 0.96 (BERT) \\
[36] & Turkish & & SIU2023-NST & ChatGPT, fine-tuned & 0.82 (BERT), 0.66 \\  & & (Turkish tweets) & BERT & (ChatGPT) \\  & & & LLaMA 2 (LoRA), & \\ Our Study & Roman Urdu & YouTube comments & M-BERT, CNN, & 0.96 (LLaMA 2) \\  & & & Bi-LSTM, SVM & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis.  
eralization. Moreover, developing multimodal offensive language detection to handle text, image, and audio together can further improve moderation. Roman Urdu shares grammatical similarities with Urdu and Hindi, and given the cross-lingual adaptation potential, further improvements can be obtained by exploring cross-lingual models. In concrete terms, the real-time deployment of LLaMA 2 with LoRA in social media moderation systems can provide for automatic filtering of offensive content. Finally, while our experiments focus on Roman Urdu, the proposed pipeline--combining customized preprocessing for orthographic variability, consensus-driven annotation, and parameter-efficient LoRA fine-tuning of transformer models--is readily transferable to other non-standard or low-resource languages. For instance, regional dialects of Korean, neologism-rich social media texts, or code-mixed scripts in other language communities exhibit similar challenges (orthographic inconsistency, high OOV rates, grammatical variability). We anticipate that, with appropriate domain-specific data and minimal adaptation of the preprocessing steps, our framework can be effectively applied to these contexts, thereby broadening its international relevance.  
Conceptualization, M.Z., N.H., A.Q., G.M., G.S. and A.G.; methodology, M.Z., N.H., A.Q., G.M., G.S. and A.G.; validation, M.Z., N.H., A.Q., G.M. and F.A.; formal analysis, M.Z., N.H., A.Q., G.M. and F.A.; data curation, M.Z., N.H., A.Q., G.M. and F.A.; writing, M.Z., N.H. and A.Q.; funding acquisition, G.S. and A.G. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Mexican Government through the grant A1-S-47854 of CONACYT, Mexico, and the grants 20254236, 20253468, and 20254341 provided by the Secretaria de Investigacion y Posgrado of the Instituto Politecnico Nacional, Mexico. The authors thank CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologias del Lenguac of the Laboratorio de Supercomputo of the INAOE, Mexico, and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award.  
The data is available on request.  
The authors declare no conflicts of interest.