## 11 Conclusions and Future Work  
This research work puts novel efforts to address cyberbullying detection problems for both implicit and explicit patterns on social media using Roman Urdu corpora comprising colloquial natural writing styles of users. Our findings contribute to analyzing and understanding colloquial patterns of low-resource Roman Urdu language and associated challenges. The study established advanced preprocessing techniques, typically developed for Roman Urdu corpora to enhance the efficiency and performance of the proposed techniques. We conducted vast experiments using seven text categorization approaches based on machine learning models and ensemble techniques and compared the performances using standard assessment metrics and visualization methods. All the experiments were carried out using Python's Pycharm IDE. Most suitable parameter values were identified using Grid search. Among ML-based text categorization, SVM with a fusion of Ngrams and Tfidf produced the most satisfactory results. In ensemble techniques, extreme gradient boosting (XGBoost) yields optimal performance with an accuracy of 78%, closely followed by the bagging technique with an accuracy score of 78%, though with relatively greater time complexity as compared to adaptive boosting (Adaboost). The time complexity of algorithms, comprising training and testing durations, was also assessed. Time evaluations indicate that LR produced the optimal execution time of 0.06 s followed by multinomial NB and Adaboost ensemble technique over a fusion of Ngrams. The probability estimations were identified for different scenarios. The major contribution of this research work is the achievement of promising results in the identification of implicit and explicit cyberbullying patterns over both classes in colloquial Roman Urdu text via vast experimentation on machine learning, voting-based ensemble methods, and hybrid features. The models proposed in this research can be implemented and embedded as social media filters to prevent or at least reduce the harassment and bullying instances in the Roman Urdu language that can cause anxiety, depression, emotional changes, and even in some cases may end up in deadly consequences. Additionally, it will help cybercrime investigative teams and centres to monitor social media content and make the internet a secure and safer place for all facets of society. In the future, we aim to investigate the role of user meta-information-related features in detecting cyberbullying patterns. We also plan to develop a web application for the demonstration of the applicability of the proposed models, allowing classifying Roman Urdu tweets and comments both in implicit and explicit forms, evaluating the implemented models and receiving user's feedback on the prediction of the models. Figure 16: Prediction probability score for explicit Roman Urdu text. **Author Contributions:** Conceptualization, A.D., M.A.M., S.B. and A.S. ([PERSON]); methodology, A.D., M.A.M., S.B., M.H., H.A., A.A. and A.S. ([PERSON]); software, A.D., M.A.M., S.B. and A.S. ([PERSON]); validation, A.D., M.H., H.A., A.A. and A.S. ([PERSON]); formal analysis, A.D. and M.A.M. ; investigation, A.D., M.A.M., S.B. and A.S. ([PERSON]); resources, M.H., H.A. and A.A.; data curation, A.D., M.H. and M.A.M. ; writing--original draft preparation, A.D., M.A.M., S.B. and A.S. ([PERSON]); writing--review and editing, M.H., H.A., A.A. and A.S. ([PERSON]); visualization, A.S. ([PERSON]); supervision, M.A.M., S.B. and A.S. ([PERSON]); project administration, M.A.M., S.B., H.A. and A.A.; funding acquisition, A.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant code (NU/RC/SERC/11/7). **Data Availability Statement:** The data that supports the findings of this research study are available on request from the corresponding author.